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# pcl_pangu: 鹏城·盘古系列模型SDK ## 简介 欢迎您使用鹏城·盘古SDK 发布版——pcl_pangu v1.2！，如果您使用过程中有任何问题请加入鹏城·盘古技术交流群进行交流[[加入](#微信交流群)]，或者在<任务>中提issue。 ## V1.2版本功能新增 [[模型即服务，鹏城·盘古系列大模型SaaS化，即Model as a Service（MaaS）](#模型即服务)] [[鹏城·盘古模型CPU部署及推理](docs/README_InferCpu.md)] pcl_pangu项目的目标是将 `鹏城·盘古`[[项目主页](https://openi.pcl.ac.cn/PCL-Platform.Intelligence/PanGu-Alpha)]开源模型、以及基于该模型的一系列任务拓展与持续学习得到的模型从语料数据预处理到模型训练推理部署整个流水线封装成开发工具包SDK。其目标与特点在于： - 向开发者提供简单易用的 鹏城·盘古应用拓展API，包括数据预处理、模型预训练、推理、微调、模型转换、模型压缩等功能。您可以快速体验鹏城·盘古大模型，并且可以轻松的开发自己的模型； - 向开发者提供基于鹏城·盘古大模型的应用拓展案例，如：鹏城.盘古增强版（Evolution）；鹏城·盘古多语言模型（mPanGu）； - 支持 mindspore 和 pytorch 框架，支持 NPU 和 GPU 设备；提供 mindspore 和 pytorch 模型转换工具，可实现鹏城·盘古模型异构框架的快速切换及兼容生态发展。 ## SDK API ## 案例教程 - [CPU推理:让个人电脑拥有与 V100 相媲美的推理速度](docs/README_InferCpu.md) - [SaaS化模型推理服务提供：支持用户快速调用SaaS服务，获得鹏城·盘古系列模型推理结果](docs/README_PGSaas.md) - [dataset 数据预处理接口：提供了从 txt 文件到训练文件格式的转换（mindrecord 格式/bin文件）](docs/README_DATASET.md) - [alpha 鹏城·盘古的基础大模型：分别在 mindspore 和 pytorch 框架下的使用教程，包括预训练、fine-tune、推理等](docs/README_PG.md) - [Evolution alpha的增强版模型： Evolution 具有更强的任务理解与处理能力](docs/README_PGE.md) - [mPangu alpha的多语言版预训练语言模型及多语言机器翻译模型：具有53种语言预训练语言模型能力及53种语言间的多对多机器翻译能力，在53种语言的单语语料进行预训练，然后在平行语料中持续学习得到，主要是以中文为中心的多语言大模型及机器翻译大模型](docs/README_MPG.md) - [model_converter 模型转换模块：提供了把 mindspore 模型转为 pytorch 模型的 api，和将 pytorch 分布式模型转为单机模型、单机模型转为分布式模型的api](docs/README_CONVERTER.md) ## 红利！！！ 免费算力申请 如果您需要华为昇腾NPU或英伟达GPU算力对pcl_pangu进行试用和测试，请登录[[启智开源社区](https://openi.pcl.ac.cn/)]，注册后进入本项目即可直接申请免费算力。 - **模型推理** 参考[[alpha_2B6模型推理](https://openi.pcl.ac.cn/PCL-Platform.Intelligence/pcl_pangu/modelarts/train-job/78547)] 参考[[mPangu_2B6模型推理](https://openi.pcl.ac.cn/PCL-Platform.Intelligence/pcl_pangu/modelarts/train-job/78689)] - **模型微调** 参考[[alpha_2B6模型训练](https://openi.pcl.ac.cn/PCL-Platform.Intelligence/pcl_pangu/modelarts/train-job/78719)] ## 模型即服务 通过我们提供Python API获取我们的模型推理SaaS服务，使用方法请参考：[**使用方法**](docs/README_PGSaas.md)。 **现阶段为免费使用，但因为服务资源有限会对总流量做限制，请勿高频调用。** 主要功能包括： - 获取所有模型信息，相关接口为model_list() - 获取指定模型信息，model_info(model) - 在线推理， generate(model, prompt_input, api_key, max_token=None, top_k=None, top_p=None, **kwargs) ## 快速上手 - **模型推理** 提供了简单的推理流程，用户只需要输入需要生成的文本就可以在进行推理。首先需要在[模型下载页面](docs/README_MODELS.md)下载对应的模型，并且解压。下面展示如何快速使用 `alpha` 模型推理： ```python from pcl_pangu.context import set_context from pcl_pangu.model import alpha, evolution, mPangu set_context(backend='onnx-cpu') config = alpha.model_config_onnx(model='2B6',load='2B6/onnx/mode/path') alpha.inference(config,input='四川的省会是?') ``` 在 mindspore/pytorch 框架上使用只需要把`set_context(backend='cpu')` 改为 `backend='mindspore'/'pytorch'`,并配置`model_config_npu/model_config_gpu` - **模型训练** 大模型的训练具有较高的入门门槛，不易上手，因此我们提供了一个简单的训练工具，可以快速的训练一个模型。用户只需要准备好训练数据，然后运行下面的代码： - 建议训练【350M模型】使用至少1卡； - 建议训练【2B6模型】使用8卡，至少2卡；默认模型并行数为2，可手工配置model_config.model_parallel_size <= 卡数 - 建议训练【13B模型】使用8卡；默认模型并行数为4，可手工配置model_config.model_parallel_size <= 卡数 ```python from pcl_pangu.context import set_context from pcl_pangu.dataset import txt2mindrecord from pcl_pangu.model import alpha, evolution, mPangu set_context(backend='mindspore') data_path = 'path/of/training/dataset' txt2mindrecord(input_glob='your/txt/path/*.txt', output_prefix=data_path) config = alpha.model_config_npu(model='350M', load='path/to/save/ckpt', data_path=data_path) alpha.train(config) ``` - **模型 fine-tuning** 模型的 fine-tuning 流程基本和训练的流程一致，只需要更改为调用 `model.fine_tune()` ： - 建议微调【350M模型】使用至少1卡； - 建议微调【2B6模型】使用8卡，至少2卡；默认模型并行数为2，可手工配置model_config.model_parallel_size <= 卡数 - 建议微调【13B模型】使用8卡；默认模型并行数为4，可手工配置model_config.model_parallel_size <= 卡数 ```python from pcl_pangu.context import set_context from pcl_pangu.dataset import txt2mindrecord from pcl_pangu.model import alpha, evolution, mPangu set_context(backend='mindspore') data_path = 'path/of/training/dataset' txt2mindrecord(input_glob='your/txt/path/*.txt', output_prefix=data_path) config = alpha.model_config_npu(model='350M', load='path/of/your/existing/ckpt', data_path=data_path) alpha.fine_tune(config) ``` ## 环境搭建 - mindspore 1、推荐使用 openi 启智社区提供的 npu 环境，[使用教程](docs/README_OPENI_MS.md)： 2、裸机使用：需要安装 mindspore 环境，并且安装： ```bash pip install pcl_pangu ``` - pytorch 如果你想在 pytorch 框架下使用，强烈建议使用我们预构建好的 docker 环境： ```bash docker pull yands/pangu-alpha-megatron-lm-nvidia-pytorch:20.03.2 or docker pull registry.cn-hangzhou.aliyuncs.com/pcl_hub/pangu_pytorch:pytorch.20.03.2 ``` 容器安装包`pip install pcl_pangu`，并使用 `/opt/conda/bin/python`。 ### 模型下载 所有模型文件下载地址：[请参考文件](./docs/README_MODELS.md) #### 微信交流群 添加微信:鹏城.盘古α交流群:
pcl_pangu/docs/000077500000000000000000000000001443434453700137715ustar00rootroot00000000000000pcl_pangu/docs/README_CONVERTER.md000066400000000000000000000035721443434453700167060ustar00rootroot00000000000000# model_converter: 模型转换模块 该模块提供了以下功能： 1、将 mindspore 框架训练的模型转换为 pytorch 框架的模型 2、将分布式训练的 pytorch 模型转为单机模型，方便推理 3、将 pytorch 框架下的单机模型转为分布式训练模型；结合第二点功能，可以实现不同分布式训练策略的转换。 ## 使用 - **将 mindspore 模型转换为 pytorch 模型** * 第一步，将mindspore 模型提取成为 numpy 文件。这个步骤需要使用 mindspore 框架，所以需要事项安装好 mindspore 环境。代码： ```python from pcl_pangu.model_converter import ms_2_numpy ms_2_numpy(ms_ckpt_path='path/of/mindspore/ckpt', npy_path='path/to/save/numpy/files') ``` `要求安装 mindspore` * 第二步，将 numpy 文件加载到 pytorch 模型里面。需要先安装好 pytorch 环境。代码: ```python from pcl_pangu.model_converter import numpy_2_pt from pcl_pangu.model import alpha alpha_config = alpha.model_config_gpu(model='2B6') numpy_2_pt(config = alpha_config, numpy_ckpt = 'path/to/numpy/files') ``` `要求安装 pytorch` - **将分布式训练的 pytorch 模型转为单机模型** ```python from pcl_pangu.model import alpha from pcl_pangu.model_converter import merge_pt alpha_config = alpha.model_config_gpu(model='2B6') merge_pt(config = alpha_config, sharding_path='path/of/your/sharding/models', num_ranks='num/of/model/rank') ``` `要求安装 pytorch` - **将单机模型转为分布式模型** ```python from pcl_pangu.model import alpha from pcl_pangu.model_converter import split_pt alpha_config = alpha.model_config_gpu(model='2B6') split_pt(config=alpha_config, merged_path='path/of/merged/model', num_ranks='num/of/rank/you/want/to/split') ``` `要求安装 pytorch` pcl_pangu/docs/README_DATASET.md000066400000000000000000000026061443434453700164210ustar00rootroot00000000000000 ### pcl_pangu：数据预处理 - 一、准备txt文本数据 - [`格式`] - 1、每段完整的内容作为一个数据sample - 2、sample之间用两个换行符\n\n隔开 - 二、数据集预处理成模型输入的要求格式 - [`mindspore-txt2mindrecord`] - 1、确认当前环境使用mindspore框架 - 2、编写数据预处理代码 - 接口参数vocab_file属于['vocab_4w', 'vocab_13w', 'your_vocab_file'], 内置了预训练4w长度的词表'vocab_4w'和 mPangu 128320长度的词表'vocab_13w'，且支持用户自定义词表。 默认使用'vocab_4w'词表，如有需要可以切换或自定义词表文件。 ```python from pcl_pangu.context import set_context from pcl_pangu.dataset import txt2mindrecord set_context(backend="mindspore") txt2mindrecord(input_glob='YOUR_DATASET_TXT_DIR/*', output_prefix='Your_Saving_DIR/mindrecord', vocab_file='vocab_4w') ``` - [`pytorch-txt2bin`] - 1、确认当前环境使用pytorch框架 - 2、编写数据预处理代码 ```python from pcl_pangu.context import set_context from pcl_pangu.dataset import txt2bin set_context(backend="pytorch") txt2bin(input_glob='YOUR_DATASET_TXT_DIR/*', output_prefix='Your_Saving_DIR/text_document') ```pcl_pangu/docs/README_InferCpu.md000066400000000000000000000046671443434453700170600ustar00rootroot00000000000000# 鹏程.盘古 CPU 推理加速 ## 简介 CPU 推理加速主要采用了 Int8 量化和 layer_past 模型加速技术。已经支持 PanGu-Alpha 2.6B、13B 模型以及 PanGu-Alpha-Evolution 2.6B 模型。开箱即用，并提供量化接口，可以快速量化模型。 要点： - CPU 推理速度接近 GPU V100 的推理速度 - CPU 推理资源需求低，2.6B 模型只需要 5G 内存，普通个人电脑都可以轻松部署 - 基于 Int8 量化技术，提升推理速度 - 基于 layer_past 模型推理加速技术，大幅度减少计算量，提升推理速度 ## 模型推理 ### 推理速度对比： | model | a100 | v100
(s/token) | Tesla T4
(s/token) | cpu/int8
(s/token) | | ----- | ---- | ----------------- | ---------------------- | --------------------- | | 2.6B | 0.06 | 0.17 | 0.37 | 0.2~0.27 + | | 13B | 0.22 | 0.70 | OOM | 0.81~1 + | #### cpu 推理资源需求 - 线程数：2 - 内存占用：2.6B模型内存占用 5GB；13B模型内存占用 21GB。 资源占用低，普通个人电脑都可以体验2.6B模型。 ### 推理 首先需要在[模型下载页面](docs/README_MODELS.md)下载对应的 cpu 模型，并且解压。下面展示如何快速进行模型推理： PanGu-Alpha 模型推理： ```python from pcl_pangu.context import set_context from pcl_pangu.model import alpha, evolution, mPangu set_context(backend='onnx-cpu') config = alpha.model_config_onnx(model='2B6'/'13B',load='onnx/mode/path') alpha.inference(config,input='四川的省会是?') ``` PanGu-Alpha-Evolution 模型推理： ```python from pcl_pangu.context import set_context from pcl_pangu.model import alpha, evolution, mPangu set_context(backend='onnx-cpu') config = evolution.model_config_onnx(model='2B6',load='onnx/mode/path') evolution.inference(config,input='四川的省会是?') ``` ## 自动量化模型 提供了将 pytorch 模型一键转为 cpu 模型的功能。 下面以 PanGu-Alpha 2.6B 模型为例，自动量化模型： ```python from pcl_pangu.context import set_context from pcl_pangu.model import alpha, evolution, mPangu from pcl_pangu.model_converter import pt_2_onnx8 set_context(backend='onnx-cpu') config = alpha.model_config_onnx(model='2B6') pt_2_onnx8(model_name='alpha-2b6', pt_path='path/of/.pt/model', model_config=config, onnx_ckpt_root_dir='output/dir/of/onnx/ckpt') ``` pcl_pangu/docs/README_MODELS.md000066400000000000000000000134331443434453700163170ustar00rootroot00000000000000**鹏程·盘古系列模型保存在 Openi 开源网站上，需要先注册成为用户才可以下载下面的模型。** ## 1、 alpha | 模型 | 大小 | 来源 |md5| | :-------------- | :---- | :------------------------------------------------------------ |:-------------------- | | 鹏程·盘古α-2.6B | 2.6G |[2.6B_part0](https://openi.pcl.ac.cn/attachments/59fac5b4-ec54-493f-95ba-d0b724f39ed6) |3904bb0e551f36206cfca96ab0f63cba | | 鹏程·盘古α-2.6B | 2.6G |[2.6B_part1](https://openi.pcl.ac.cn/attachments/6535b42b-67b5-469b-9506-067c5c5768e0) |19407cbc33ee4929d68f500e015869cc | | 鹏程·盘古α-2.6B | 2.6G |[2.6B_part2](https://openi.pcl.ac.cn/attachments/4dd9178d-43cb-47aa-b299-1c0bfe38c343) |9ce140312f962079e13f91f681a269ad | | 鹏程·盘古α-2.6B | 2.6G |[2.6B_part3](https://openi.pcl.ac.cn/attachments/c1acd001-03ec-4484-ab31-4b3636603d50) |290158ffc426a89b7e357018c701e71d | | 鹏程·盘古α-2.6B | 391M |[2.6B_word_embedding](https://openi.pcl.ac.cn/attachments/12319797-b0ce-4563-99e6-4f6a7d3ec0cb) |9ca91161bde9c089dd8304db0428455e | | 鹏程·盘古α-2.6B | 10M |[2.6B_top_query_embedding](https://openi.pcl.ac.cn/attachments/287dc9a4-8c81-4132-ad4d-d2e350701ba3) |320b401050319c1c63117c36b93651c1 | | 鹏程·盘古α-2.6B | 10M |[2.6B_position_embedding](https://openi.pcl.ac.cn/attachments/1f0f3dcb-9d82-4d59-84c0-3d6bade7de2f) |bf7eb84b7b9fd6004cb22260883ab080 | | 鹏程·盘古α-13B | 12G |[13B_part0](https://openi.pcl.ac.cn/attachments/341df568-12e7-4dad-afa4-612bd9181b41) |724bdaf87e0eb4aa9ca0f35e8af92464 | | 鹏程·盘古α-13B | 12G |[13B_part1](https://openi.pcl.ac.cn/attachments/bc6142f0-dee3-41bd-98b3-d8c9f75a6843) |493579a1ccc2a16a616cf04daedd031f | | 鹏程·盘古α-13B | 12G |[13B_part2](https://openi.pcl.ac.cn/attachments/15be215d-d488-4eee-9780-aea3c56efa47) |de4e8a023c56b7134bff4dfbcc230871 | | 鹏程·盘古α-13B | 12G |[13B_part3](https://openi.pcl.ac.cn/attachments/695f935a-5b4e-42e2-adaf-962fcb742382) |6a6bcfdadf5a0d23c567d6959bf4eaba | | 鹏程·盘古α-13B | 781M |[13B_word_embedding](https://openi.pcl.ac.cn/attachments/a4f86058-86a7-40de-8b39-6c35242b206f) |a4e0c5893d255500396dfa1bfe7ba3ba | | 鹏程·盘古α-13B | 20M |[13B_top_query_embedding](https://openi.pcl.ac.cn/attachments/22d49fa2-5aa9-4d3f-9d77-3f491350ffc0) |4d75c3b42f85db825cebdc99329160bd | | 鹏程·盘古α-13B | 20M |[13B_position_embedding](https://openi.pcl.ac.cn/attachments/76e796fa-0d97-48ea-8e2c-f49bb2f2b21b) |299ee4174cc4de7fdebec1c3e631e348 | | 鹏程·盘古α-200B | ~T | 敬请期待 | | | 模型 | 参数 | 平台 | 模型文件 | 大小 | | ------ | ------ | ------- | ------ | --- | | 鹏程·盘古α-350M | 350M | pytorch | [pangu-alpha_350m_fp16-pytorch.zip](https://openi.pcl.ac.cn/attachments/6e516012-18be-4576-a54e-570218bb3dbd) | 600M | | 鹏程·盘古α-2.6B |2.6B | pytorch | [Pangu-alpha_2.6B_fp16_mgt.zip](https://openi.pcl.ac.cn/attachments/72aec03d-6bdb-4652-ac2a-8099db4b0bed) | 4.6G | | 鹏程·盘古α-13B |13B | pytorch | [Pangu-alpha_13B_fp16_mgt.zip](https://openi.pcl.ac.cn/attachments/937b3e2d-98fb-4871-9691-b32afb5a4d79?type=0) | 22G | | 模型 | 参数 | 平台 | 模型文件 | 大小 | | ------ | ------ | ------- | ------ | --- | | 鹏程·盘古α-2.6B |2.6B | cpu/onnx | [onnx_int8_pangu_alpha_2b6.zip](https://openi.pcl.ac.cn/attachments/2de25370-a3bb-4544-88a5-3a3b74d8cac6?type=0) | 2.8G | | 鹏程·盘古α-13B |13B | cpu/onnx | [onnx_int8_pangu_alpha_13b.zip](https://openi.pcl.ac.cn/attachments/3f8bd196-c685-426c-bdc6-a106d1ebed1c?type=0) | 13.1G | ## 2、 evolution | 参数 | 平台| 模型文件| 大小 | |-------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------| --- | | 2.6B | pytorch | [pangu-alpha-evolution_2.6B_fp16.zip](https://openi.pcl.ac.cn/attachments/a8c58df2-92c5-4aa2-827f-69a24691ae2c?type=0") | 4.8G | | 参数 | 平台| 模型文件| 大小 | |-------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------|------| | 2.6B | cpu/onnx | [onnx_int8_pangu_evolution_2b6.zip](https://openi.pcl.ac.cn/attachments/452d8bf4-54d9-4e11-a008-654b7e3d0382?type=0") | 2.8G | ## 3、 mPanGu ### 多语言预训练模型 | 参数 | 平台 | 模型文件 | 大小 | | ------ | ------- |------------------------------------------------------------------------------------------------------------------------| --- | |2.6B | Mindspore | [mPanGu_Alpha-53-Pretrain_fp16.ckpt](https://openi.pcl.ac.cn/attachments/42fbe1b7-8d61-4126-a74b-dd754fdc47e1?type=0) | 6.0G | |2.6B | Mindspore | [mPanGu_Alpha-53-Pretrain_fp32.ckpt](https://openi.pcl.ac.cn/attachments/584e1945-a01c-4f08-a9b6-9bd30f0e21be?type=0) | 12G | ### 多语言翻译模型 | 参数 | 平台 | 模型文件 | 大小 | | ------ | ------- | ------ | --- | |2.6B | Mindspore | [mPanGu_Alpha-53_fp16.ckpt](https://openi.pcl.ac.cn/attachments/3fa0d3cb-bda6-4bd1-8c24-bab2600518c0?type=0) | 6.0G | |2.6B | Mindspore | [mPanGu_Alpha-53_fp32.ckpt](https://openi.pcl.ac.cn/attachments/584e1945-a01c-4f08-a9b6-9bd30f0e21be?type=0) | 12G | pcl_pangu/docs/README_MPG.md000066400000000000000000000103661443434453700157610ustar00rootroot00000000000000# 鹏程.mPanGu-α 53 ## 简介 鹏程.mPanGu-α-53基于一带一路多语言翻译场景应用，支持多语言翻译任务的预训练、迁移学习、微调，支持在NPU/GPU上基于mindspore分布式训练（最少8卡）、 推理（全精度/FP16，1卡），单模型支持53种语言任意两种语言间的互译。 相关原理和方法，请参见[[项目主页](https://openi.pcl.ac.cn/PCL-Platform.Intelligence/mPanGu-Alpha-53)]。 **要点：** - 通过多语种模板匹配支持单语、单双语混合、双语训练模式，模型支持任两语种间的互译； - 提供了基于盘古模型进行单语预训练和单双语混合增强训练的新范式，扩充低资源语种的语料多样性; - 鹏程.mPanGu-α，参数量2.6B，学习多语种通用的跨语言表示，在低资源语种翻译效果上提升明显。 ## 使用方法 ### 环境搭建 原生支持基于mindspore的NPU/GPU环境，通过模型转换也可支持基于pytorch的GPU环境，环境信息：[请参考项目主页](../README.md)。 ### 数据准备 如果基于鹏程.mPanGu-α做微调或者基于鹏程.mPanGu-α直接训练，请参考如下的所有数据准备过程，基于鹏程.mPanGu-α做推理预测只需要参考‘模型预测’流程即可。 - step1, 基于统任务格式将任务数据转换成文本生成形式，也可以直接下我们提供的已处理好的样本数据做调试实验。 ``` 示例： 转换后数据： 单语zh.txt：'Mary Ng （伍凤仪），加拿大华裔联邦国会议员，几天前刚刚被委任加拿大小型企业及出口促进部部长。' 双语zh-en_corpus.txt：'第三，无功绩。\t third, with no success.' ``` - step2，生成txt文本数据，便于step3程序的处理。 ``` 每段完整的内容作为一个数据样本。 样本间用两个换行符\n\n隔开 ``` - step3，txt数据转化为mindRecord文件格式，将文本数据转换成能直接输入模型的数值数据。 ```python from pcl_pangu.context import set_context from pcl_pangu.dataset import txt2mindrecord set_context(backend="mindspore") txt2mindrecord(input_glob='YOUR_DATASET_TXT_DIR/*', \ output_prefix='Your_Saving_DIR/text_document', \ vocab_file='../tokenizer/spm_13w/vocab') ``` ### 模型训练 如果你想利用鹏程.mPanGu-α做训练，可以参考如下实现过程。 ```python from pcl_pangu.context import set_context from pcl_pangu.dataset import txt2mindrecord from pcl_pangu.model import mPangu set_context(backend="mindspore") data_path = 'path/of/training/dataset' txt2mindrecord(input_glob='your/txt/path/*.txt', \ output_prefix=data_path, \ vocab_file='../tokenizer/spm_13w/vocab') config = mPangu.model_config_npu(model='2B6', save='path/to/save/ckpt', data_path=data_path) mPangu.train(config) ``` ### 模型微调 如果你想基于鹏程.mPanGu-α用自己的数据做进一步的增量训练，可以参考如下实现过程。 首先需要在[模型下载页面](docs/README_MODELS.md)下载对应的`鹏程.mPanGu-α`多语言翻译模型。 模型微调流程基本和训练的流程一致，只需要更改为调用 `*.fine_tune()`。 ```python from pcl_pangu.context import set_context from pcl_pangu.dataset import txt2mindrecord from pcl_pangu.model import mPangu set_context(backend="mindspore") data_path = 'path/of/training/dataset' txt2mindrecord(input_glob='your/txt/path/*.txt', \ output_prefix=data_path, \ vocab_file='../tokenizer/spm_13w/vocab') config = mPangu.model_config_npu(model='2B6', save='path/of/your/existing/ckpt', data_path=data_path) mPangu.fine_tune(config) ``` ### 模型预测 提供了简单的推理流程，用户只需要输入需要生成的文本就可以在进行推理。首先需要在[模型下载页面](docs/README_MODELS.md)下载对应的模型。 ```python from pcl_pangu.context import set_context from pcl_pangu.model import mPangu set_context(backend='mindspore') config = mPangu.model_config_npu(model='2B6',save='2B6/mode/path') mPangu.inference(config,input='四川的省会是?', src_language='zh', tag_language='kk') ``` pcl_pangu/docs/README_OPENI_MS.md000066400000000000000000000010611443434453700165770ustar00rootroot00000000000000如果你想在 mindspore 框架下使用，强烈建议使用openi启智社区提供的 npu 容器环境[1/2/4/8卡]，镜像选择[Mindspore-1.3.0-c78-python3.7-euleros2.8-aarch64]:
在项目根目录下，编写环境预安装脚本ma-pre-start.sh脚本，npu容器环境会在执行.py脚本前，预先执行该.sh脚本进行环境配置，sh脚本内容如下： ```bash WORK_DIR=YOUR_CODE_DIR LOCAL_DIR=$(cd "$(dirname "$0")";pwd) cd ${LOCAL_DIR}/${WORK_DIR}/ pip install pcl_pangu ``` 使用`/usr/bin/python`。pcl_pangu/docs/README_PG.md000066400000000000000000000070541443434453700156440ustar00rootroot00000000000000# 鹏程.盘古(PanGu-Alpha) ## 简介 「鹏程·盘古α」由以鹏城实验室为首的技术团队联合攻关，首次基于“鹏城云脑Ⅱ”和国产mindSpore框架的自动混合并行模式实现在2048卡算力集群上的大规模分布式训练，训练出业界首个2000亿参数以中文为核心的预训练生成语言模型。鹏程·盘古α预训练模型支持丰富的场景应用，在知识问答、知识检索、知识推理、阅读理解等文本生成领域表现突出，具备很强的小样本学习能力。 相关原理和方法，请参见[[项目主页](https://openi.pcl.ac.cn/PCL-Platform.Intelligence/PanGu-Alpha)]。 ## 使用方法 ### 环境搭建 目前仅支持pytorch 和 mindspore。环境信息：[请参考项目主页](../README.md)。 ### 数据准备 如果基于鹏程.盘古做微调或者基于鹏程.盘古直接训练，请参考下面的数据准备过程。 - step1，组织训练预料的格式；转为txt文本数据，txt 文件格式如下： ``` 每段完整的内容作为一个数据样本。 样本间用两个换行符\n\n隔开 ``` - step2，数值化，将文本数据转换成能直接输入模型的数值数据。 pytorch ```python from pcl_pangu.context import set_context from pcl_pangu.dataset import txt2bin set_context(backend="pytorch") txt2bin(input_glob='YOUR_DATASET_TXT_DIR/*.txt', output_prefix='Your_Saving_DIR/text_document') ``` ### 训练 如果你想利用鹏程.盘古的技术对预训练语言模型如鹏程.盘古做增强训练，可以参考如下实现过程。 首先需要在[模型下载页面](./README_MODELS.md)下载对应的`鹏程.盘古`模型，并且解压。 【注意】多卡训练需要配置 model_config 当中的节点数"nnodes"和每个节点卡数"nproc_per_node" ```python from pcl_pangu.context import set_context from pcl_pangu.dataset import txt2bin from pcl_pangu.model import alpha set_context(backend="pytorch") #or backend='mindspore' data_path = 'path/of/training/dataset' txt2bin(input_glob='your/txt/path/*.txt', output_prefix=data_path) config = alpha.model_config_gpu(model='350M', nnodes=1, nproc_per_node=4, model_parallel_size=1, save='path/to/save/ckpt', data_path=data_path) alpha.train(config) ``` ### 微调 如果你想基于鹏程.盘古对你任务数据做进一步的增量训练，可以参考如下实现过程。 首先需要在[模型下载页面](./README_MODELS.md)下载对应的`鹏程.盘古`模型，并且解压。 模型微调流程基本和训练的流程一致，只需要更改为调用 `model.fine_tune()`。 ```python from pcl_pangu.context import set_context from pcl_pangu.dataset import txt2bin from pcl_pangu.model import alpha set_context(backend="pytorch") data_path = 'path/of/training/dataset' txt2bin(input_glob='your/txt/path/*.txt', output_prefix=data_path) config = alpha.model_config_gpu(model='2B6', save='path/of/your/existing/ckpt', data_path=data_path) alpha.fine_tune(config) ``` ### 预测 如果你想利用鹏程.盘古直接用于下游的推理，可以参考如下实现过程。 首先需要在[模型下载页面](./README_MODELS.md)下载对应的`鹏程.盘古`模型，并且解压。 ```python from pcl_pangu.context import set_context from pcl_pangu.model import alpha set_context(backend='pytorch') config = alpha.model_config_gpu(model='2B6', save='2B6/mode/path') alpha.inference(config, input="四川的省会是?") ``` pcl_pangu/docs/README_PGE.md000066400000000000000000000110751443434453700157470ustar00rootroot00000000000000# 鹏程.盘古增强版(PanGu-Alpha-Evolution) ## 简介 盘古增强版，更强的任务理解与处理能力，更加擅长遵循您的任务说明。 目前盘古增强版2.6B GPU版本的模型推理阶段显存占用控制在7G以内，8G显存的机器即可推理。 相关原理和方法，请参见[[项目主页](https://openi.pcl.ac.cn/PCL-Platform.Intelligence/PanGu-Alpha-Evolution)]。 **要点：** - 创新应用了多任务学习、任务统一格式、提示微调和持续学习技术，对基本版模型进行了能力扩展和增强，性能得到大幅提升。 - 提供了基于盘古大模型进行提示微调和持续学习的性能优化新范式，更好的识别用户的任务说明，同时做到尽量保持模型的原始生成能力。 - 盘古增强版，参数量2.6B，在中英翻译、开放域知识问答、文本分类、摘要生成等能力提升显著，在一张V100 GPU卡上可以完成多路并行推理。 ## 使用方法 ### 环境搭建 目前仅支持pytorch，环境信息：[请参考项目主页](../README.md)。 ### 数据准备 如果基于鹏程.盘古增强版做微调或者基于鹏程.盘古直接训练，请参考如下的所有数据准备过程。如果基于鹏程.盘古增强版做推理预测，则只需要参考step1即可。 - step1, 基于统一任务格式将任务数据转换成文本生成形式，也可以直接下载我们提供的已处理好的[样本数据](https://openi.pcl.ac.cn/attachments/c11e6124-f94c-4124-b3c0-af1133952cb2?type=0)做调试实验。 ``` 示例： NLI任务，转换后样例："推理关系判断：\n前提：新的权利已经足够好了\n假设：每个人都很喜欢最新的福利\n选项：矛盾，蕴含，中立\n答案：中立" 分类任务，转换后样例："文本分类：\n基本上可以说是诈骗\n选项：积极，消极\n答案：消极" QA任务，转换后样例："问题：\n郑州是那个省的\n答案：河南" 翻译任务，转换后样例："翻译成英文：\n不过他承认，美国与欧洲关系密切。\nHe admitted that the United States was relatively friendly with Europe." ``` - step2，生成txt文本数据，便于step3程序的处理。 ``` 每段完整的内容作为一个数据样本。 样本间用两个换行符\n\n隔开 ``` - step3，数值化，将文本数据转换成能直接输入模型的数值数据。 pytorch ```python from pcl_pangu.context import set_context from pcl_pangu.dataset import txt2bin set_context(backend="pytorch") txt2bin(input_glob='YOUR_DATASET_TXT_DIR/*', output_prefix='Your_Saving_DIR/text_document') ``` ### 训练 如果你想利用鹏程.盘古增强版的技术对预训练语言模型如鹏程.盘古做增强训练，可以参考如下实现过程。 首先需要在[模型下载页面](./README_MODELS.md)下载对应的`鹏程.盘古基本版`模型，并且解压。 ```python from pcl_pangu.context import set_context from pcl_pangu.dataset import txt2bin from pcl_pangu.model import evolution set_context(backend="pytorch") data_path = 'path/of/training/dataset' txt2bin(input_glob='your/txt/path/*.txt', output_prefix=data_path) config = evolution.model_config_gpu(model='350M', save='path/to/save/ckpt', data_path=data_path) evolution.train(config) ``` ### 微调 如果你想基于鹏程.盘古增强版对你的任务数据做进一步的增量训练，可以参考如下实现过程。 首先需要在[模型下载页面](./README_MODELS.md)下载对应的`鹏程.盘古增强版`模型，并且解压。 模型微调流程基本和训练的流程一致，只需要更改为调用 `model.fine_tune()`。 ```python from pcl_pangu.context import set_context from pcl_pangu.dataset import txt2bin from pcl_pangu.model import evolution set_context(backend="pytorch") data_path = 'path/of/training/dataset' txt2bin(input_glob='your/txt/path/*.txt', output_prefix=data_path) config = evolution.model_config_gpu(model='2B6', save='path/of/your/existing/ckpt', data_path=data_path) evolution.fine_tune(config) ``` ### 预测 如果你想利用鹏程.盘古增强版直接用于下游任务的推理，可以参考如下实现过程。 首先需要在[模型下载页面](./README_MODELS.md)下载对应的`鹏程.盘古增强版`模型，并且解压。 ```python from pcl_pangu.context import set_context from pcl_pangu.model import evolution set_context(backend='pytorch') config = evolution.model_config_gpu(model='2B6', load='2B6/mode/path') evolution.inference(config, input="文本分类：\n基本上可以说是诈骗\n选项：积极，消极\n答案：") ``` pcl_pangu/docs/README_PGSaas.md000066400000000000000000000031621443434453700164500ustar00rootroot00000000000000# 鹏程.盘古系列大模型Saas化 ## 简介 您可以通过我们提供Python API获取我们的模型Saas服务。目前支持模型如下： - 鹏程·盘古Alpha 13B，prompt输入构造请参见[[项目主页](https://openi.pcl.ac.cn/PCL-Platform.Intelligence/PanGu-Alpha)]。 - 鹏程·盘古增强版 2.6B，prompt输入构造请参见[[项目主页](https://openi.pcl.ac.cn/PCL-Platform.Intelligence/PanGu-Alpha-Evolution)]。 ### 获取api_key api_key是用于获取型Saas服务的凭证，用户在使用在线推理api前请先申请api_key，获取方法参见[[api_key获取指南](./api_key获取指南.doc)]。目前api_key暂定为免费申请，后续可能会调整。 ### 获取模型信息 如果不了解我们目前提供Saas服务，可以通过以下接口获取。 ```python from pcl_pangu.online import ModelInfo # 获取所有在线模型的基本信息 models = ModelInfo.model_list() # 获取指定在线模型的基本信息 model = ModelInfo.model_info(model="pangu-alpha-13B-md") ``` ### 推理 如果你想体验鹏程·盘古【增强版】SaaS服务，可以参考如下实现过程： ```python from pcl_pangu.online import Infer model = "pangu-alpha-evolution-2B6-pt" prompt_input = "文本分类：\n基本上可以说是诈骗\n选项：积极，消极\n答案：" result = Infer.generate(model, prompt_input, api_key) # api_key获取请见上文 ``` 同理，鹏程·盘古【alpha】SaaS服务使用如下： ```python from pcl_pangu.online import Infer model = "pangu-alpha-13B-md" prompt_input = "你好" result = Infer.generate(model, prompt_input, api_key) # api_key获取请见上文 ```pcl_pangu/docs/README_mPanGu.md000066400000000000000000000103221443434453700165150ustar00rootroot00000000000000# 鹏程.盘古增强版(PanGu-Alpha-Evolution) ## 简介 鹏程.mPanGu-α-53基于一带一路多语言翻译场景应用，支持多语言翻译任务的预训练、迁移学习、微调，支持在NPU/GPU上基于mindspore分布式训练（最少8卡）、 推理（全精度/FP16，1卡），单模型支持53种语言任意两种语言间的互译。 相关原理和方法，请参见[[项目主页](https://openi.pcl.ac.cn/PCL-Platform.Intelligence/mPanGu-Alpha-53)]。 **要点：** - 通过多语种模板匹配支持单语、单双语混合、双语训练模式，模型支持任两语种间的互译； - 提供了基于盘古模型进行单语预训练和单双语混合增强训练的新范式，扩充低资源语种的语料多样性; - 鹏程.mPanGu-α，参数量2.6B，学习多语种通用的跨语言表示，在低资源语种翻译效果上提升明显。 ## 使用方法 ### 环境搭建 原生支持基于mindspore的NPU/GPU环境，通过模型转换也可支持基于pytorch的GPU环境，环境信息：[请参考项目主页](../README.md)。 ### 数据准备 如果基于鹏程.mPanGu-α做微调或者基于鹏程.mPanGu-α直接训练，请参考如下的所有数据准备过程，基于鹏程.mPanGu-α做推理预测只需要参考‘模型预测’流程即可。 - step1, 基于统任务格式将任务数据转换成文本生成形式，也可以直接下我们提供的已处理好的[样本数据](https://openi.pcl.ac.cn/attachments/c11e6124-f94c-4124-b3c0-af1133952cb2?type=0)做调试实验。 ``` 示例： 转换后数据： 单语zh.txt：'Mary Ng （伍凤仪），加拿大华裔联邦国会议员，几天前刚刚被委任加拿大小型企业及出口促进部部长。' 双语zh-en_corpus.txt：'第三，无功绩。\t third, with no success.' ``` - step2，生成txt文本数据，便于step3程序的处理。 ``` 每段完整的内容作为一个数据样本。 样本间用两个换行符\n\n隔开 ``` - step3，配置vocab_file为'vocab_13w', txt数据转化为mindrecord文件格式，将文本数据转换成能直接输入模型的数值数据。 ```python from pcl_pangu.context import set_context from pcl_pangu.dataset import txt2mindrecord set_context(backend="mindspore") txt2mindrecord(input_glob='YOUR_DATASET_TXT_DIR/*', \ output_prefix='Your_Saving_DIR/text_document', \ vocab_file='vocab_13w') ``` ### 模型训练 如果你想利用鹏程.mPanGu-α做训练，可以参考如下实现过程。 ```python from pcl_pangu.context import set_context from pcl_pangu.dataset import txt2mindrecord from pcl_pangu.model import mPangu set_context(backend="mindspore") data_path = 'path/of/training/dataset' txt2mindrecord(input_glob='your/txt/path/*.txt', \ output_prefix=data_path, \ vocab_file='vocab_13w') config = mPangu.model_config_npu(model='2B6', save='path/to/save/ckpt', data_path=data_path) mPangu.train(config) ``` ### 模型微调 如果你想基于鹏程.mPanGu-α用自己的数据做进一步的增量训练，可以参考如下实现过程。 首先需要在[模型下载页面](docs/README_MODELS.md)下载对应的`鹏程.mPanGu-α`多语言翻译模型。 模型微调流程基本和训练的流程一致，只需要更改为调用 `model.fine_tune()`。 ```python from pcl_pangu.context import set_context from pcl_pangu.dataset import txt2mindrecord from pcl_pangu.model import mPangu set_context(backend="mindspore") data_path = 'path/of/training/dataset' txt2mindrecord(input_glob='your/txt/path/*.txt', \ output_prefix=data_path, \ vocab_file='vocab_13w') config = mPangu.model_config_npu(model='2B6', save='path/of/your/existing/ckpt', data_path=data_path) mPangu.fine_tune(config) ``` ### 模型预测 提供了简单的推理流程，用户只需要输入需要生成的文本就可以在进行推理。首先需要在[模型下载页面](docs/README_MODELS.md)下载对应的模型。 ```python from pcl_pangu.context import set_context from pcl_pangu.model import mPangu set_context(backend='mindspore') config = mPangu.model_config_npu(model='2B6',save='2B6/mode/path') mPangu.inference(config,input='四川的省会是?', src_language='zh', tag_language='kk') ``` pcl_pangu/docs/api_key获取指南.doc000066400000000000000000016430001443434453700221760ustar00rootroot00000000000000ࡱ> U Rj bjbjnn2aa 448  ll+++++___$8_____++4&UUU_++QU_UU:,+D!|'( =<0l  :(__U_____U___l_____________4Q : api_keyScWS ekN(uOmȉhV{vU_/Tzfs^Shttps://git.openi.org.cn/ (W*NN4YP NpQ (W NbFh-N b n 0  ekN(WQsvLub N b ^(u QsY NLub0  ek NeQ NLr Ty 6qTpQ ubNLr c Oub~rW̑bN2NW[&{2N b Neg sS:Napi_key0      "( 0 4 < D H J L N P T V Z \ ݷݨzkPHDHDHh2jh2U5h2h2B* OJQJaJfH`o(ph'oq jh2h2OJQJUh2h2B*OJQJph h2h2B*OJQJo(phh2B*OJQJo(phh2h2B*OJQJphj(h2h2OJQJUh2OJQJjh2h2OJQJUh2h2OJQJh2OJQJo(h2h2CJ OJQJaJ H L N R T X Z ^ ` d f h j gd2$a$gd2\ ` b h j 5h2h2B* OJQJaJfH`o(ph'oq jh2Uh20182P. 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    pip install megatron-lm
    ## Downloading Checkpoints We've provided two pretrained checkpoints for use to evaluate or finetuning downstream tasks. To access these checkpoints, first please [sign up](https://ngc.nvidia.com/signup) for and [setup](https://ngc.nvidia.com/setup/installers/cli) the NVIDIA GPU Cloud (NGC) Registry CLI. The checkpoints can be downloaded with: 
    ngc registry model download-version --dest <output_base_directory> nvidia/<model_name>:<version>
    The available models along with `:` are below: * [BERT-345M](https://ngc.nvidia.com/catalog/models/nvidia:megatron_bert_345m): megatron\_bert\_345m:v0.0 * [GPT-2-345M](https://ngc.nvidia.com/catalog/models/nvidia:megatron_lm_345m): megatron\_lm\_345m:v0.0 The models require vocabulary files to run. The BERT uncased WordPiece vocab file can be extracted from Google's [pretrained BERT models](https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt). The GPT-2 [vocab file](https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json) and [merge table](https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt) can be downloaded directly. Further documentation for downloading models can be found in the [NGC documentation](https://docs.nvidia.com/dgx/ngc-registry-cli-user-guide/index.html#topic_6_4_1) # Usage After installation, there are several possible workflows. The most comprehensive is: 1. Data preprocessing 2. Pretraining 3. Finetuning (Optional for zero-shot tasks) 4. Downstream task evaluation or text generation However, steps 1 and 2 can be replaced by using one of the pretrained models mentioned above. We've provided several scripts for pretraining both BERT and GPT-2 in [`examples`](./examples) directory, as well as scripts for both zero-shot and fine-tuned downstream tasks including MNLI, RACE, WikiText103, and LAMBADA evaluation. There is also a script for GPT-2 interactive text generation. # Training ## Data Preprocessing We support three file formats for training, but all require preprocessing. First, place your training data in a loose json format, with one json containing a text sample per line. For example: 
    {"src": "www.nvidia.com", "text": "The quick brown fox", "type": "Eng", "id": "0", "title": "First Part"}
{"src": "The Internet", "text": "jumps over the lazy dog", "type": "Eng", "id": "42", "title": "Second Part"}
    The name of the `text` field of the json can be changed by using the `--json-key` flag in [`preprocess_data.py`](tools/preprocess_data_pangu.py) The other metadata are optional and are not used in training. The loose json is then processed into a binary format for training. To convert the json into mmap, cached index file, or the lazy loader format use `preprocess_data.py`. Set the `--dataset-impl` flag to `mmap`, `cached`, or `lazy`, respectively (default is `mmap`). An example script to prepare data for BERT training is: 
    python tools/preprocess_data.py \
       --input my-corpus.json \
       --output-prefix my-bert \
       --vocab bert-vocab.txt \
       --dataset-impl mmap \
       --tokenizer-type BertWordPieceLowerCase \
       --split-sentences
    The output will be two files named, in this case, `my-bert_text_sentence.bin` and `my-bert_text_sentence.idx`. The `--data-path` specified in later BERT training is the full path and new filename, but without the file extension. Some minor modifications are required for GPT-2 data preprocessing, namely, the addition of a merge table, an end-of-document token, removal of sentence splitting, and a change to the tokenizer type: 
    python tools/preprocess_data.py \
       --input my-corpus.json \
       --output-prefix my-gpt2 \
       --vocab gpt2-vocab.json \
       --dataset-impl mmap \
       --tokenizer-type GPT2BPETokenizer \
       --merge-file gpt2-merges.txt \
       --append-eod
    Here the output files are named `my-gpt2_text_document.bin` and `my-gpt2_text_document.idx`. As before, in GPT-2 training, use the longer name without the extension as `--data-path`. Further command line arguments are described in the source file [`preprocess_data.py`](tools/preprocess_data_pangu.py). ## BERT Pretraining `bash examples/pretrain_bert.sh` This script runs single GPU 345M parameter BERT pretraining. Debugging is the primary use for single GPU training, as the code base and command line arguments are optimized for highly distributed training. Most of the arguments are fairly self-explanatory. By default, the learning rate decays linearly over the training iterations starting at `--lr` to a minimum set by `--min-lr` over `--lr-decay-iters` iterations. The fraction of training iterations used for warmup is set by `--warmup`. While this is single GPU training, the batch size specified by `--batch-size` is per GPU used for data parallelism. The data is partitioned into a 949:50:1 ratio for training/validation/test sets (default is 969:30:1). This partitioning happens on the fly, but is consistent across runs with the same random seed (1234 by default, or specified manually with `--seed`). The logging, checkpoint-saving, and evaluation intervals are specified. Checkpointing the activations facilitates the training of larger models and/or batches. Note that the `--data-path` now includes the additional `_text_sentence` suffix added in preprocessing, but does not include the file extensions. 
    CHECKPOINT_PATH=checkpoints/bert_345m
VOCAB_FILE=bert-vocab.txt
DATA_PATH=my-bert_text_sentence

BERT_ARGS="--num-layers 24 \
           --hidden-size 1024 \
           --num-attention-heads 16 \
           --seq-length 512 \
           --max-position-embeddings 512 \
           --lr 0.0001 \
           --train-iters 2000000 \
           --min-lr 0.00001 \
           --lr-decay-iters 990000 \
           --warmup 0.01 \
           --batch-size 8 \
           --vocab-file $VOCAB_FILE \
           --split 949,50,1 \
           --fp16"

OUTPUT_ARGS="--log-interval 10 \
             --save-interval 500 \
             --eval-interval 100 \
             --eval-iters 10 \
             --checkpoint-activations"

python pretrain_bert.py \
       $BERT_ARGS \
       $OUTPUT_ARGS \
       --save $CHECKPOINT_PATH \
       --load $CHECKPOINT_PATH \
       --data-path $DATA_PATH
    Further command line arguments are described in the source file [`arguments.py`](./megatron/arguments.py). ## GPT-2 Pretraining `bash examples/pretrain_gpt2.sh` This script runs single GPU 345M parameter GPT-2 pretraining. As mentioned above, single GPU training is primarily intended for debugging purposes, as the code is optimized for distributed training. It follows largely the same format as the previous BERT script with a few notable differences: the tokenization scheme used is BPE (which requires a merge table and a `json` vocabulary file) instead of WordPiece, the model architecture allows for longer sequences (note that the max position embedding must be greater than or equal to the maximum sequence length), and the `--lr-decay-style` has been set to cosine decay. Note that the `--data-path` now includes the additional `_text_document` suffix added in preprocessing, but does not include the file extensions. 
    CHECKPOINT_PATH=checkpoints/gpt2_345m
VOCAB_FILE=gpt2-vocab.json
MERGE_FILE=gpt2-merges.txt
DATA_PATH=my-gpt2_text_document

GPT2_ARGS="--num-layers 24 \
           --hidden-size 1024 \
           --num-attention-heads 16 \
           --seq-length 1024 \
           --max-position-embeddings 1024 \
           --batch-size 4 \
           --lr 0.00015 \
           --train-iters 500000 \
           --lr-decay-iters 320000 \
           --lr-decay-style cosine \
           --vocab-file $VOCAB_FILE \
           --merge-file $MERGE_FILE \
           --warmup .01 \
           --fp16"

OUTPUT_ARGS=<same as those in BERT pretraining above>

python pretrain_gpt2.py \
       $GPT2_ARGS \
       $OUTPUT_ARGS \
       --save $CHECKPOINT_PATH \
       --load $CHECKPOINT_PATH \
       --data-path $DATA_PATH \
    Further command line arguments are described in the source file [`arguments.py`](./megatron/arguments.py). ## Distributed BERT or GPT-2 Pretraining `bash examples/pretrain_bert_distributed.sh` `bash examples/pretrain_gpt2_distributed.sh` These scripts use the PyTorch distributed launcher for distributed training. As such, multinode training can be achieved by properly setting environment variables and using `init_method='env://'` in the launcher. See the official PyTorch [documentation](https://pytorch.org/docs/stable/distributed.html#launch-utility) for further description of these [environment variables](https://pytorch.org/docs/stable/distributed.html#environment-variable-initialization). By default, multinode training uses the [nccl](https://developer.nvidia.com/nccl) distributed backend. A simple set of additional arguments and the use of the PyTorch distributed module with the Python flag `-m torch.distributed.launch`, detailed below, are the only additional requirements to adopt distributed training. The two tiers of parallelism are data and model parallelism. First, we facilitate two distributed data parallel implementations: a simple one of our own that performs gradient all-reduce at the end of back propagation step, and Torch's distributed data parallel wrapper that overlaps gradient reduction with back propagation computation. To switch between these two options use `--DDP-impl local` or `--DDP-impl torch`, respectively. As expected, Torch distributed data parallelism is more efficient at larger model parallel sizes. For example, for the 8.3 billion parameters model running on 512 GPUs, the scaling increases from 60% to 76% when Torch's distributed data parallel is used. However, the overlapping method requires more memory and for some configurations (e.g., 2.5 billion parameters using 2-way model parallel and 1.2 billion parameters with no model parallel) can make the overall training slower as a result. We empirically found that using a smaller model in those cases improves the training time. Second, we developed a simple and efficient intra-layer model parallel approach. To use model parallelism, add the `--model-parallel-size` flag to specify the number of GPUs among which to split the model, along with the arguments passed to the distributed launcher as mentioned above. With `WORLD_SIZE` GPUs and `MP_SIZE` model parallel size, `WORLD_SIZE`/`MP_SIZE` GPUs will be used for data parallelism. The default value for `--model-parallel-size` is 1, which will not implement model parallelism. Other than these minor changes, the distributed training is identical to the training on a single GPU. Distributed BERT training: 
    WORLD_SIZE=8
MP_SIZE=2

DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \
                  --nnodes 1 \
                  --node_rank 0 \
                  --master_addr localhost \
                  --master_port 6000"

CHECKPOINT_PATH=checkpoints/bert_345m
VOCAB_FILE=bert-vocab.txt
DATA_PATH=my-bert_text_sentence
BERT_ARGS=<same as those in BERT pretraining above>
OUTPUT_ARGS=<same as those in BERT pretraining above>

python -m torch.distributed.launch $DISTRIBUTED_ARGS ./pretrain_bert.py \
                $BERT_ARGS \
                $OUTPUT_ARGS \
                --save $CHECKPOINT_PATH \
                --load $CHECKPOINT_PATH \
                --data-path $DATA_PATH \
                --model-parallel-size $MP_SIZE \
                --DDP-impl torch
    Distributed GPT-2 training: 
    WORLD_SIZE=8
MP_SIZE=2

DISTRIBUTED_ARGS=<same as those directly above>

CHECKPOINT_PATH=checkpoints/gpt2_345m
VOCAB_FILE=gpt2-vocab.json
MERGE_FILE=gpt2-merges.txt
DATA_PATH=my-gpt2_text_document
GPT2_ARGS=<same as those in GPT-2 pretraining above>
OUTPUT_ARGS=<same as those in BERT pretraining above>

python -m torch.distributed.launch $DISTRIBUTED_ARGS ./pretrain_gpt2.py \
                $GPT2_ARGS \
                $OUTPUT_ARGS \
                --save $CHECKPOINT_PATH \
                --load $CHECKPOINT_PATH \
                --data-path $DATA_PATH \
                --model-parallel-size $MP_SIZE \
                --DDP-impl torch
    ## REALM Pipeline We are working on implementing the [REALM](https://arxiv.org/pdf/2002.08909.pdf) system. The following sections (will) reflect the three stages of training it. For now it's just the ICT code. Loosely, they are pretraining the retriever modules, then jointly training the language model and the retriever, and then finetuning a question answering head on the language model with fixed retriever. ### Inverse Cloze Task (ICT) Pretraining 1. Have a corpus in loose JSON format with the intention of creating a collection of fixed-size blocks of text as the fundamental units of data. For a corpus like Wikipedia, this will mean multiple sentences per block but also multiple blocks per document. Run `tools/preprocess_data.py` to construct one or more indexed datasets with the `--split-sentences` argument to make sentences the basic unit. For the original REALM system, we construct two datasets, one with the title of every document, and another with the body. Refer to the following script 
    python preprocess_data.py \
    --input /path/to/corpus.json \
    --json-keys text title \
    --split-sentences \
    --tokenizer-type BertWordPieceLowerCase \
    --vocab-file /path/to/vocab.txt \
    --output-prefix corpus_indexed \
    --workers 5  # works well for 10 CPU cores. Scale up accordingly.
    2. Use a custom samples mapping function in place of `megatron/data/realm_dataset_utils.get_block_samples_mapping` if required. To do this, you will need to implement a new function in C++ inside of `megatron/data/helpers.cpp`. The samples mapping data structure is used to select the data that will constitute every training sample in advance of the training loop. The samples mapping is responsible for holding all of the required metadata needed to construct the sample from one or more indexed datasets. In REALM, the samples mapping contains the start and end sentence indices, as well as the document index (to find the correct title for a body) and a unique ID for every block. 3. Pretrain a BERT language model using `pretrain_bert.py`, with the sequence length equal to the block size in token ids. This model should be trained on the same indexed dataset that is used to supply the blocks for the information retrieval task. In REALM, this is an uncased bert base model trained with the standard hyperparameters. 4. Use `pretrain_ict.py` to train an `ICTBertModel` which uses two BERT-based encoders to encode queries and blocks to perform retrieval with. The script below trains the ICT model from REALM. It refrences a pretrained BERT model (step 3) in the `--bert-load` argument. The batch size used in the paper is 4096, so this would need to be run with data parallel world size 32. 
    python pretrain_ict.py \
    --num-layers 12 \
    --num-attention-heads 12 \
    --hidden-size 768 \
    --batch-size 128 \
    --seq-length 256 \
    --max-position-embeddings 256 \
    --ict-head-size 128 \
    --train-iters 100000 \
    --checkpoint-activations \
    --bert-load /path/to/pretrained_bert \
    --load checkpoints \
    --save checkpoints \
    --data-path /path/to/indexed_dataset \
    --titles-data-path /path/to/titles_indexed_dataset \
    --vocab-file /path/to/vocab.txt \
    --lr 0.0001 \
    --num-workers 2 \
    --lr-decay-style linear \
    --weight-decay 1e-2 \
    --clip-grad 1.0 \
    --warmup .01 \
    --save-interval 3000 \
    --query-in-block-prob 0.1 \
    --fp16
    ### Building an Index of Block Embeddings After having trained an ICT model, you can now embed an entire dataset of blocks by creating a `BlockData` structure. After that has been saved, you can load it and wrap it with a `FaissMIPSIndex` to do fast similarity search which is key in the learned information retrieval pipeline. The initial index can be built with the following script, meant to be run in an interactive session. It can leverage multiple GPUs on multiple nodes to index large datasets much more quickly. 
    python tools/create_doc_index.py \
    --num-layers 12 \
    --hidden-size 768 \
    --ict-head-size 128 \
    --num-attention-heads 12 \
    --batch-size 128 \
    --checkpoint-activations \
    --seq-length 256 \
    --max-position-embeddings 256 \
    --ict-load /path/to/pretrained_ict \
    --data-path /path/to/indexed_dataset \
    --titles-data-path /path/to/titles_indexed_dataset \
    --block-data-path embedded_blocks.pkl \
    --indexer-log-interval 1000 \
    --indexer-batch-size 128 \
    --vocab-file /path/to/vocab.txt \
    --num-workers 2 \
    --fp16
    # Evaluation and Tasks We provide several command line arguments, detailed in the scripts listed below, to handle various zero-shot and fine-tuned downstream tasks. However, you can also finetune your model from a pretrained checkpoint on other corpora as desired. To do so, simply add the `--finetune` flag and adjust the input files and training parameters within the original training script. The iteration count will be reset to zero, and the optimizer and internal state will be reinitialized. If the fine-tuning is interrupted for any reason, be sure to remove the `--finetune` flag before continuing, otherwise the training will start again from the beginning. Because evaluation requires substantially less memory than training, it may be advantageous to merge a model trained in parallel for use on a single GPU in downstream tasks. The following script accomplishes this. 
    MODEL_PARALLEL_SIZE=2

VOCAB_FILE=bert-vocab.txt
CHECKPOINT_PATH=checkpoints/bert_345m

WORLD_SIZE=$MODEL_PARALLEL_SIZE python tools/merge_mp_partitions.py \
        --model-type BERT \
        --model-parallel-size $MODEL_PARALLEL_SIZE \
        --tokenizer-type BertWordPieceLowerCase \
        --vocab-file $VOCAB_FILE \
        --num-layers 24 \
        --hidden-size 1024 \
        --num-attention-heads 16 \
        --seq-length 512 \
        --max-position-embeddings 512 \
        --load $CHECKPOINT_PATH
    Several downstream tasks are described for both GPT-2 and BERT models below. They can be run in distributed and model parallel modes with the same changes used in the training scripts. ## GPT-2 Text Generation `bash examples/generate_text.sh` We generate text samples using largely the GPT-2 pretraining script. Few changes need to make, such as we need to provide the path to the pretrained checkpoint, the length of the output samples, whether to generate texts unconditionally (`--num-samples` to denote how many samples to generate) or conditional (need to pass `--sample-input-file ` where each line of the file will be used as the conditional texts). There are few optional parameters to play, e.g. `top-k`, `top-p`, or `greedy` (set top-k and top-p to 0) sampling.. 
    CHECKPOINT_PATH=checkpoints/gpt2_345m
VOCAB_FILE=gpt2-vocab.json
MERGE_FILE=gpt2-merges.txt
GPT2_ARGS=<same as those in GPT-2 pretraining above>

MAX_OUTPUT_SEQUENCE_LENGTH=1024
TEMPERATURE=1.0
TOP_P=0.9
NUMBER_OF_SAMPLES=2
OUTPUT_FILE=samples.json

python tools/generate_samples_gpt2.py \
       $GPT2_ARGS \
       --load $CHECKPOINT_PATH \
       --out-seq-length $MAX_OUTPUT_SEQUENCE_LENGTH \
       --temperature $TEMPERATURE \
       --genfile $OUTPUT_FILE \
       --num-samples $NUMBER_OF_SAMPLES \
       --top_p $TOP_P \
       --recompute
    ## GPT-2 Evaluation We include example scripts for GPT-2 evaluation on WikiText perplexity evaluation and LAMBADA Cloze accuracy. ### WikiText Perplexity Evaluation For even comparison with prior works, we evaluate perplexity on the word-level [WikiText-103 test dataset](https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-v1.zip), and appropriately compute perplexity given the change in tokens when using our subword tokenizer. We use the following command to run WikiText-103 evaluation on a 345M parameter model. 
    TASK="WIKITEXT103"

VALID_DATA=<wikitext path>.txt
VOCAB_FILE=gpt2-vocab.json
MERGE_FILE=gpt2-merges.txt
CHECKPOINT_PATH=checkpoints/gpt2_345m

COMMON_TASK_ARGS="--num-layers 24 \
                  --hidden-size 1024 \
                  --num-attention-heads 16 \
                  --seq-length 1024 \
                  --max-position-embeddings 1024 \
                  --fp16 \
                  --vocab-file $VOCAB_FILE"

python tasks/main.py \
       --task $TASK \
       $COMMON_TASK_ARGS \
       --valid-data $VALID_DATA \
       --tokenizer-type GPT2BPETokenizer \
       --merge-file $MERGE_FILE \
       --load $CHECKPOINT_PATH \
       --batch-size 8 \
       --checkpoint-activations \
       --log-interval 10 \
       --no-load-optim \
       --no-load-rng
    ### LAMBADA Cloze Accuracy To compute LAMBADA cloze accuracy (the accuracy of predicting the last token given the preceeding tokens) we utilize a detokenized, processed version of the [LAMBADA dataset](https://github.com/cybertronai/bflm/blob/master/lambada_test.jsonl). We use the following command to run LAMBADA evaluation on a 345M parameter model. Note that the `--strict-lambada` flag should be used to require whole word matching. Make that `lambada` is part of the file path. 
    TASK="LAMBADA"

VALID_DATA=<lambada path>.json
VOCAB_FILE=gpt2-vocab.json
MERGE_FILE=gpt2-merges.txt
CHECKPOINT_PATH=checkpoints/gpt2_345m
COMMON_TASK_ARGS=<same as those in WikiText Perplexity Evaluation above>

python tasks/main.py \
       --task $TASK \
       $COMMON_TASK_ARGS \
       --valid-data $VALID_DATA \
       --tokenizer-type GPT2BPETokenizer \
       --strict-lambada \
       --merge-file $MERGE_FILE \
       --load $CHECKPOINT_PATH \
       --batch-size 8 \
       --checkpoint-activations \
       --log-interval 10 \
       --no-load-optim \
       --no-load-rng
    Further command line arguments are described in the source file [`main.py`](./tasks/main.py) ## BERT Task Evaluation ### RACE Evaluation The following script finetunes the BERT model for evaluation on the [RACE dataset](http://www.cs.cmu.edu/~glai1/data/race/). The `TRAIN_DATA` and `VALID_DATA` directory contain the RACE dataset as separate `.txt` files. 
    TRAIN_DATA="data/RACE/train/middle"
VALID_DATA="data/RACE/dev/middle \
            data/RACE/dev/high"
VOCAB_FILE=bert-vocab.txt
PRETRAINED_CHECKPOINT=checkpoints/bert_345m
CHECKPOINT_PATH=checkpoints/bert_345m_race
COMMON_TASK_ARGS="--num-layers 24 \
                  --hidden-size 1024 \
                  --num-attention-heads 16 \
                  --seq-length 512 \
                  --max-position-embeddings 512 \
                  --fp16 \
                  --vocab-file $VOCAB_FILE"

COMMON_TASK_ARGS_EXT="--train-data $TRAIN_DATA \
                      --valid-data $VALID_DATA \
                      --pretrained-checkpoint $PRETRAINED_CHECKPOINT \
                      --checkpoint-activations \
                      --save-interval 10000 \
                      --save $CHECKPOINT_PATH \
                      --log-interval 100 \
                      --eval-interval 1000 \
                      --eval-iters 10 \
                      --weight-decay 1.0e-1"

python tasks/main.py \
       --task RACE \
       $COMMON_TASK_ARGS \
       $COMMON_TASK_ARGS_EXT \
       --tokenizer-type BertWordPieceLowerCase \
       --epochs 3 \
       --batch-size 4 \
       --lr 1.0e-5 \
       --warmup 0.06
    ### MNLI Evaluation The following script finetunes the BERT model for evaluation with the [MultiNLI sentence pair corpus](https://www.nyu.edu/projects/bowman/multinli/). Because the matching tasks are quite similar, the script can be quickly tweaked to work with the [Quora Question Pairs](https://www.kaggle.com/quora/question-pairs-dataset) (QQP) dataset as well. 
    
TRAIN_DATA="data/glue_data/MNLI/train.tsv"
VALID_DATA="data/glue_data/MNLI/dev_matched.tsv \
            data/glue_data/MNLI/dev_mismatched.tsv"
PRETRAINED_CHECKPOINT=checkpoints/bert_345m
VOCAB_FILE=bert-vocab.txt
CHECKPOINT_PATH=checkpoints/bert_345m_mnli
COMMON_TASK_ARGS=<same as those in RACE Evaluation above>
COMMON_TASK_ARGS_EXT=<same as those in RACE Evaluation above>

python tasks/main.py \
       --task MNLI \
       $COMMON_TASK_ARGS \
       $COMMON_TASK_ARGS_EXT \
       --tokenizer-type BertWordPieceLowerCase \
       --epochs 5 \
       --batch-size 8 \
       --lr 5.0e-5 \
       --warmup 0.065
    # Datasets We do not host any datasets for GPT-2 or BERT training, however, we detail their collection so that our results may be reproduced. ## Collecting Wikipedia Training Data We recommend following the Wikipedia data extraction process specified by Google research: "the recommended pre-processing is to download [the latest dump](https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2), extract the text with [WikiExtractor.py](https://github.com/attardi/wikiextractor), and then apply any necessary cleanup to convert it into plain text." We recommend using the `--json` argument when using WikiExtractor, which will dump the Wikipedia data into loose json format (one json per line), making it more manageable on the file system and also readily consumable by our codebase. We recommend further preprocessing this json dataset by nltk punctuation standardization. For BERT training, add newlines between sentences during data preprocessing. This is done with the `--split-sentences` flag in `preprocess_data.py` as described [above](#data-preprocessing). (Note that if you'd like to use Wikipedia data for GPT-2 training you should still clean it with nltk/spacy/ftfy, but do not split it into newline separated sentences.) ## Collecting GPT-2 Webtext Data We utilize the publicly available [OpenWebText](https://github.com/eukaryote31/openwebtext) library from [jcpeterson](https://github.com/jcpeterson/openwebtext) and [eukaryote31's](https://github.com/eukaryote31/openwebtext) work to download urls. We then filtered, cleaned, and deduplicated all downloaded content according to the procedure described in our [openwebtext](./tools/openwebtext) directory. For reddit URLs corresponding to content up to October 2018 we arrived at approximately 37GB of content. pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/__init__.py000066400000000000000000000000001443434453700240400ustar00rootroot00000000000000pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/examples/000077500000000000000000000000001443434453700235575ustar00rootroot00000000000000pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/examples/evaluate_zeroshot_gpt2.sh000066400000000000000000000021551443434453700306150ustar00rootroot00000000000000#!/bin/bash WORLD_SIZE=8 DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \ --nnodes 1 \ --node_rank 0 \ --master_addr localhost \ --master_port 6000" TASK="LAMBADA" VALID_DATA= VOCAB_FILE=gpt2-vocab.json MERGE_FILE=gpt2-merges.txt CHECKPOINT=checkpoints/gpt2_345m python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \ --task $TASK \ --valid-data $VALID_DATA \ --tokenizer-type GPT2BPETokenizer \ --strict-lambada \ --vocab-file $VOCAB_FILE \ --merge-file $MERGE_FILE \ --load $CHECKPOINT \ --model-parallel-size 1 \ --num-layers 24 \ --hidden-size 1024 \ --num-attention-heads 16 \ --batch-size 8 \ --checkpoint-activations \ --seq-length 1024 \ --max-position-embeddings 1024 \ --log-interval 10 \ --fp16 \ --no-load-optim \ --no-load-rng pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/examples/finetune_mnli_distributed.sh000066400000000000000000000030071443434453700313510ustar00rootroot00000000000000#!/bin/bash WORLD_SIZE=8 DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \ --nnodes 1 \ --node_rank 0 \ --master_addr localhost \ --master_port 6000" TRAIN_DATA="data/glue_data/MNLI/train.tsv" VALID_DATA="data/glue_data/MNLI/dev_matched.tsv \ data/glue_data/MNLI/dev_mismatched.tsv" PRETRAINED_CHECKPOINT=checkpoints/bert_345m VOCAB_FILE=bert-vocab.txt CHECKPOINT_PATH=checkpoints/bert_345m_mnli python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \ --task MNLI \ --seed 1234 \ --train-data $TRAIN_DATA \ --valid-data $VALID_DATA \ --tokenizer-type BertWordPieceLowerCase \ --vocab-file $VOCAB_FILE \ --epochs 5 \ --pretrained-checkpoint $PRETRAINED_CHECKPOINT \ --model-parallel-size 1 \ --num-layers 24 \ --hidden-size 1024 \ --num-attention-heads 16 \ --batch-size 8 \ --checkpoint-activations \ --lr 5.0e-5 \ --lr-decay-style linear \ --warmup 0.065 \ --seq-length 512 \ --max-position-embeddings 512 \ --save-interval 500000 \ --save $CHECKPOINT_PATH \ --log-interval 10 \ --eval-interval 100 \ --eval-iters 50 \ --weight-decay 1.0e-1 \ --fp16 pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/examples/finetune_pangu_distributed.sh000066400000000000000000000036241443434453700315310ustar00rootroot00000000000000#! /bin/bash #export CUDA_VISIBLE_DEVICES="0,1" # Runs the "345M" parameter model GPUS_PER_NODE=8 # Change for multinode config MASTER_ADDR=localhost MASTER_PORT=6000 NNODES=1 NODE_RANK=0 WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES)) #DATA_PATH=/userhome/dataset/megatron/zhanghan/sample_100G_policy_3/Sample100GPolicy3_text_document DATA_PATH=/ghome/yands/dataset/megatron/test_vocab4w/text_document #CHECKPOINT_PATH=/ghome/yands/model/checkPoints/megatron-1.1-pangu CHECKPOINT_PATH=/root/pangu/model/pangu_fp16_8mp_2b6 #CHECKPOINT_PATH=/userhome/model/checkPoints/megatron-1.1-pangu-2.6B/merged_split #CHECKPOINT_PATH=/userhome/model/panguAlpha_2.6b_fp16_NumpyCkpt/merged/ DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE --nnodes $NNODES --node_rank $NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT" /opt/conda/bin/python -u -m torch.distributed.launch $DISTRIBUTED_ARGS \ pretrain_gpt2.py \ --model-parallel-size 8 \ --num-layers 31 \ --hidden-size 2560 \ --num-attention-heads 32 \ --batch-size 1 \ --seq-length 1024 \ --max-position-embeddings 1024 \ --train-iters 500000 \ --lr-decay-iters 320000 \ --save $CHECKPOINT_PATH \ --load $CHECKPOINT_PATH \ --data-path $DATA_PATH \ --vocab-file /userhome/pclproject/gpt/Megatron-LM-1.1/megatron/tokenizer/bpe_4w_pcl/vocab \ --merge-file gpt2-merges.txt \ --data-impl mmap \ --split 949,50,1 \ --distributed-backend nccl \ --lr 0.00015 \ --lr-decay-style cosine \ --min-lr 1.0e-5 \ --weight-decay 1e-2 \ --clip-grad 1.0 \ --warmup .01 \ --checkpoint-activations \ --log-interval 100 \ --save-interval 1000 \ --eval-interval 1000 \ --eval-iters 10 \ --attention-dropout 0.1 \ --hidden-dropout 0.1 \ --reset-attention-mask \ --finetune set +x pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/examples/finetune_race_distributed.sh000066400000000000000000000031141443434453700313230ustar00rootroot00000000000000#!/bin/bash WORLD_SIZE=8 DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \ --nnodes 1 \ --node_rank 0 \ --master_addr localhost \ --master_port 6000" TRAIN_DATA="data/RACE/train/middle" VALID_DATA="data/RACE/dev/middle \ data/RACE/dev/high" VOCAB_FILE=bert-vocab.txt PRETRAINED_CHECKPOINT=checkpoints/bert_345m CHECKPOINT_PATH=checkpoints/bert_345m_race python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \ --task RACE \ --seed 1234 \ --train-data $TRAIN_DATA \ --valid-data $VALID_DATA \ --tokenizer-type BertWordPieceLowerCase \ --vocab-file $VOCAB_FILE \ --epochs 3 \ --pretrained-checkpoint $PRETRAINED_CHECKPOINT \ --model-parallel-size 1 \ --num-layers 24 \ --hidden-size 1024 \ --num-attention-heads 16 \ --batch-size 4 \ --checkpoint-activations \ --lr 1.0e-5 \ --lr-decay-style linear \ --warmup 0.06 \ --seq-length 512 \ --max-position-embeddings 512 \ --save-interval 100000 \ --save $CHECKPOINT_PATH \ --log-interval 10 \ --eval-interval 100 \ --eval-iters 50 \ --weight-decay 1.0e-1 \ --clip-grad 1.0 \ --hidden-dropout 0.1 \ --attention-dropout 0.1 \ --fp16 pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/examples/generate_text.sh000066400000000000000000000014731443434453700267560ustar00rootroot00000000000000#!/bin/bash export CUDA_VISIBLE_DEVICES="4" CHECKPOINT_PATH=/userhome/model/checkPoints/megatron-1.1-pangu-2.6B/merged/ python tools/generate_samples_gpt2.py \ --model-parallel-size 1 \ --num-layers 31 \ --hidden-size 2560 \ --load $CHECKPOINT_PATH \ --num-attention-heads 32 \ --max-position-embeddings 1024 \ --tokenizer-type GPT2BPETokenizer \ --fp16 \ --batch-size 2 \ --seq-length 1024 \ --out-seq-length 1024 \ --temperature 1.0 \ --vocab-file /userhome/pclproject/gpt/Megatron-LM-1.1/megatron/tokenizer/bpe_4w_pcl/vocab \ --merge-file gpt2-merges.txt \ --genfile unconditional_samples.json \ --num-samples 0 \ --top_p 0.9 \ --recompute \ --no-load-rng \ --sample-input-file "" pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/examples/merge_mp_bert.sh000066400000000000000000000013761443434453700267310ustar00rootroot00000000000000#!/bin/bash MODEL_PARALLEL_SIZE=2 VOCAB_FILE=bert-vocab.txt CHECKPOINT_PATH=checkpoints/bert_345m WORLD_SIZE=$MODEL_PARALLEL_SIZE python tools/merge_mp_partitions.py \ --model-type BERT \ --model-parallel-size $MODEL_PARALLEL_SIZE \ --tokenizer-type BertWordPieceLowerCase \ --vocab-file $VOCAB_FILE \ --num-layers 24 \ --hidden-size 1024 \ --num-attention-heads 16 \ --seq-length 512 \ --max-position-embeddings 512 \ --load $CHECKPOINT_PATH pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/examples/pretrain_bert.sh000066400000000000000000000015761443434453700267640ustar00rootroot00000000000000#!/bin/bash RANK=0 WORLD_SIZE=1 DATA_PATH=_text_sentence CHECKPOINT_PATH= python pretrain_bert.py \ --num-layers 24 \ --hidden-size 1024 \ --num-attention-heads 16 \ --batch-size 4 \ --seq-length 512 \ --max-position-embeddings 512 \ --train-iters 2000000 \ --save $CHECKPOINT_PATH \ --load $CHECKPOINT_PATH \ --data-path $DATA_PATH \ --vocab-file bert-vocab.txt \ --data-impl mmap \ --split 949,50,1 \ --distributed-backend nccl \ --lr 0.0001 \ --min-lr 0.00001 \ --lr-decay-style linear \ --lr-decay-iters 990000 \ --weight-decay 1e-2 \ --clip-grad 1.0 \ --warmup .01 \ --log-interval 100 \ --save-interval 10000 \ --eval-interval 1000 \ --eval-iters 10 \ --fp16 pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/examples/pretrain_bert_distributed.sh000066400000000000000000000023441443434453700313600ustar00rootroot00000000000000#!/bin/bash GPUS_PER_NODE=8 # Change for multinode config MASTER_ADDR=localhost MASTER_PORT=6000 NNODES=1 NODE_RANK=0 WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES)) DATA_PATH=_text_sentence CHECKPOINT_PATH= DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE --nnodes $NNODES --node_rank $NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT" python -m torch.distributed.launch $DISTRIBUTED_ARGS \ pretrain_bert.py \ --model-parallel-size 1 \ --num-layers 24 \ --hidden-size 1024 \ --num-attention-heads 16 \ --batch-size 4 \ --seq-length 512 \ --max-position-embeddings 512 \ --train-iters 1000000 \ --save $CHECKPOINT_PATH \ --load $CHECKPOINT_PATH \ --data-path $DATA_PATH \ --vocab-file bert-vocab.txt \ --data-impl mmap \ --split 949,50,1 \ --distributed-backend nccl \ --lr 0.0001 \ --lr-decay-style linear \ --min-lr 1.0e-5 \ --lr-decay-iters 990000 \ --weight-decay 1e-2 \ --clip-grad 1.0 \ --warmup .01 \ --log-interval 100 \ --save-interval 10000 \ --eval-interval 1000 \ --eval-iters 10 \ --fp16 pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/examples/pretrain_gpt2.sh000066400000000000000000000017661443434453700267050ustar00rootroot00000000000000#! /bin/bash # Runs the "345M" parameter model RANK=0 WORLD_SIZE=1 DATA_PATH=_text_document CHECKPOINT_PATH= python pretrain_gpt2.py \ --num-layers 24 \ --hidden-size 1024 \ --num-attention-heads 16 \ --batch-size 8 \ --seq-length 1024 \ --max-position-embeddings 1024 \ --train-iters 500000 \ --lr-decay-iters 320000 \ --save $CHECKPOINT_PATH \ --load $CHECKPOINT_PATH \ --data-path $DATA_PATH \ --vocab-file gpt2-vocab.json \ --merge-file gpt2-merges.txt \ --data-impl mmap \ --split 949,50,1 \ --distributed-backend nccl \ --lr 0.00015 \ --min-lr 1.0e-5 \ --lr-decay-style cosine \ --weight-decay 1e-2 \ --clip-grad 1.0 \ --warmup .01 \ --checkpoint-activations \ --log-interval 100 \ --save-interval 10000 \ --eval-interval 1000 \ --eval-iters 10 \ --fp16 set +x pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/examples/pretrain_gpt2_distributed_2.6B.sh000066400000000000000000000030241443434453700317630ustar00rootroot00000000000000#! /bin/bash export CUDA_VISIBLE_DEVICES="2,3,4,5" GPUS_PER_NODE=1 # Change for multinode config MASTER_ADDR=localhost MASTER_PORT=6000 NNODES=1 NODE_RANK=0 WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES)) DATA_PATH=/userhome/dataset/megatron/zhanghan/sample_100G_policy_3/Sample100GPolicy3_text_document CHECKPOINT_PATH=/userhome/model/checkPoints/megatron-1.1-pangu-2.6B/merged DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE --nnodes $NNODES --node_rank $NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT" python -m torch.distributed.launch $DISTRIBUTED_ARGS \ pretrain_gpt2.py \ --model-parallel-size 1 \ --num-layers 31 \ --hidden-size 2560 \ --num-attention-heads 32 \ --batch-size 4 \ --seq-length 1024 \ --max-position-embeddings 1024 \ --train-iters 500000 \ --lr-decay-iters 320000 \ --save $CHECKPOINT_PATH \ --load $CHECKPOINT_PATH \ --data-path $DATA_PATH \ --vocab-file /userhome/pclproject/gpt/Megatron-LM-1.1/megatron/tokenizer/bpe_4w_pcl/vocab \ --merge-file gpt2-merges.txt \ --data-impl mmap \ --split 949,50,1 \ --distributed-backend nccl \ --lr 0.00015 \ --lr-decay-style cosine \ --min-lr 1.0e-5 \ --weight-decay 1e-2 \ --clip-grad 1.0 \ --warmup .01 \ --log-interval 100 \ --save-interval 300 \ --eval-interval 1000 \ --eval-iters 10 \ --fp16 \ --reset-attention-mask \ --checkpoint-activations set +x pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/examples/pretrain_gpt2_distributed_350M.sh000066400000000000000000000030211443434453700317750ustar00rootroot00000000000000#! /bin/bash export CUDA_VISIBLE_DEVICES="2,3,4,5" # Runs the "345M" parameter model GPUS_PER_NODE=4 # Change for multinode config MASTER_ADDR=localhost MASTER_PORT=6000 NNODES=1 NODE_RANK=0 WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES)) DATA_PATH=/ghome/yands/dataset/megatron/zhanghan/sample_100G_policy_3/Sample100GPolicy3_text_document CHECKPOINT_PATH=/ghome/yands/model/checkPoints/megatron-1.1-pangu DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE --nnodes $NNODES --node_rank $NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT" python -m torch.distributed.launch $DISTRIBUTED_ARGS \ pretrain_gpt2.py \ --model-parallel-size 2 \ --num-layers 24 \ --hidden-size 1024 \ --num-attention-heads 16 \ --batch-size 8 \ --seq-length 1024 \ --max-position-embeddings 1024 \ --train-iters 500000 \ --lr-decay-iters 320000 \ --save $CHECKPOINT_PATH \ --load $CHECKPOINT_PATH \ --data-path $DATA_PATH \ --vocab-file /userhome/pclproject/gpt/Megatron-LM-1.1/megatron/tokenizer/bpe_4w_pcl/vocab \ --merge-file gpt2-merges.txt \ --data-impl mmap \ --split 949,50,1 \ --distributed-backend nccl \ --lr 0.00015 \ --lr-decay-style cosine \ --min-lr 1.0e-5 \ --weight-decay 1e-2 \ --clip-grad 1.0 \ --warmup .01 \ --checkpoint-activations \ --log-interval 100 \ --save-interval 1000 \ --eval-interval 1000 \ --eval-iters 10 \ --fp16 set +x pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/examples/pretrain_pangu_distributed_2.6B.sh000066400000000000000000000030041443434453700322170ustar00rootroot00000000000000#! /bin/bash export CUDA_VISIBLE_DEVICES="2,3,4,5" GPUS_PER_NODE=1 # Change for multinode config MASTER_ADDR=localhost MASTER_PORT=6000 NNODES=1 NODE_RANK=0 WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES)) DATA_PATH=/userhome/dataset/megatron/zhanghan/sample_100G_policy_3/Sample100GPolicy3_text_document CHECKPOINT_PATH=/userhome/model/checkPoints/megatron-1.1-pangu-2.6B/merged DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE --nnodes $NNODES --node_rank $NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT" python -m torch.distributed.launch $DISTRIBUTED_ARGS \ pretrain_gpt2.py \ --model-parallel-size 1 \ --num-layers 31 \ --hidden-size 2560 \ --num-attention-heads 32 \ --batch-size 4 \ --seq-length 1024 \ --max-position-embeddings 1024 \ --train-iters 500000 \ --lr-decay-iters 320000 \ --save $CHECKPOINT_PATH \ --load $CHECKPOINT_PATH \ --data-path $DATA_PATH \ --vocab-file /userhome/pclproject/gpt/Megatron-LM-1.1/megatron/tokenizer/bpe_4w_pcl/vocab \ --merge-file gpt2-merges.txt \ --data-impl mmap \ --split 949,50,1 \ --distributed-backend nccl \ --lr 0.00015 \ --lr-decay-style cosine \ --min-lr 1.0e-5 \ --weight-decay 1e-2 \ --clip-grad 1.0 \ --warmup .01 \ --log-interval 100 \ --save-interval 300 \ --eval-interval 1000 \ --eval-iters 10 \ --reset-attention-mask \ --checkpoint-activations set +x pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/images/000077500000000000000000000000001443434453700232065ustar00rootroot00000000000000pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/images/Makefile000066400000000000000000000006501443434453700246470ustar00rootroot00000000000000default: cases.png scaling-mp.png scaling-dp.png # for some 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جلالهجمجمحجمىجميجىجيححجحجيحمحمىحميحىحيخخجخحخمخىخيدذذٰررسولرٰریالزسسجسجحسجىسحسحجسخسخىسخيسرسمسمجسمحسممسهسىسيششجشجيشحشحمشحيشخشرشمشمخشممشهشىشيصصحصححصحيصخصرصلعمصلىصلى الله عليه 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-10.1681 营 -10.1682 ▁深刻 -10.1682 ▁啥 -10.1683 ▁信心 -10.1686 ▁夹 -10.1686 ▁咬 -10.17 创 -10.1702 ▁简 -10.1703 ▁轮 -10.1703 ▁货币 -10.1704 ▁地下 -10.1706 ▁坚定 -10.1707 众 -10.1708 ▁Sm -10.171 ▁一大 -10.1711 轩 -10.1713 冰 -10.1714 ▁一人 -10.1717 ▁观 -10.1717 ▁账号 -10.1719 裤 -10.1721 ▁经理 -10.1721 ▁逐步 -10.1727 ▁舒服 -10.1731 ▁一套 -10.1732 ▁领先 -10.1733 ▁差不多 -10.1733 ▁必然 -10.1735 ▁亲自 -10.1737 槸 -10.1746 冷 -10.175 备 -10.1757 ▁机器人 -10.1757 ▁一时 -10.1761 潮 -10.1783 ▁灯 -10.1785 ▁画面 -10.1788 ▁引发 -10.1788 ▁冲击 -10.179 ▁采访 -10.1792 ▁秘密 -10.1792 ▁通讯 -10.1794 ▁熟 -10.1794 p -10.1795 ▁不锈钢 -10.1795 ▁荣 -10.1807 ▁大大 -10.1811 ▁名称 -10.1814 ▁四川 -10.1815 ▁地球 -10.1826 ▁头发 -10.1827 ▁营业 -10.1827 罪 -10.1829 ▁截至 -10.183 ▁尺寸 -10.1831 ▁隐 -10.1832 ▁法院 -10.1837 ▁歌 -10.1838 ▁如下 -10.1847 ▁两位 -10.1855 ▁商务 -10.1859 ▁股东 -10.186 ▁黑暗 -10.1864 ▁厂商 -10.1865 ▁泰 -10.1868 ▁挡 -10.1874 ▁CO -10.1882 ▁利润 -10.1889 乡 -10.1893 ▁袁 -10.1895 ▁围绕 -10.1895 h -10.1895 ▁医 -10.1896 ▁一项 -10.1898 ▁不由 -10.1902 er -10.1902 ▁乌 -10.1904 ▁丹 -10.1909 ▁公子 -10.191 ▁总体 -10.1914 ▁市民 -10.1915 持 -10.1917 材料 -10.1919 ▁专利 -10.1921 ▁身影 -10.1921 ▁卫 -10.1926 ▁描述 -10.1927 ▁扑 -10.1927 ▁Er -10.1928 直 -10.193 方面 -10.1932 ▁亿美元 -10.1937 ▁留学 -10.1937 ▁停车 -10.1939 ▁内心 -10.1945 ▁一生 -10.1949 ▁替 -10.1953 ▁对外 -10.1957 ▁怀疑 -10.1958 ▁不然 -10.1958 x -10.196 唇 -10.196 棒 -10.1965 ▁护 -10.197 ▁广场 -10.1973 京 -10.1981 ▁殿 -10.1983 ▁胜 -10.1987 ▁遭 -10.199 沙 -10.1991 绝 -10.1996 泰 -10.2 烈 -10.2001 ▁便利 -10.2002 ▁這 -10.2008 ▁再说 -10.2008 ▁冯 -10.2014 ▁浓 -10.2018 像 -10.2021 末 -10.2022 ▁厨房 -10.2024 ▁公益 -10.2025 Y -10.2033 项目 -10.2033 ▁虚 -10.2033 ▁权益 -10.2038 ▁列 -10.2039 ▁环节 -10.204 环境 -10.2041 ▁链接 -10.2043 ▁QQ -10.2044 ▁考核 -10.2047 ▁学历 -10.2047 ▁私服 -10.205 ▁呆 -10.2052 ▁躲 -10.2053 ▁电商 -10.2057 ▁线上 -10.2061 ▁穿着 -10.2067 刚 -10.2069 ▁爆发 -10.2074 ▁铝 -10.2079 ▁监控 -10.208 凤 -10.2083 ▁没事 -10.2086 ▁伸手 -10.2087 ▁电力 -10.2092 ▁好奇 -10.2094 ▁论 -10.2096 ▁照顾 -10.2097 ▁一口气 -10.2097 险 -10.2101 ▁好看 -10.2101 ▁爬 -10.2104 厚 -10.2113 ▁反正 -10.2113 ▁尊重 -10.2115 ▁居 -10.2119 段时间 -10.2122 ▁驱动 -10.2124 金属 -10.2128 ▁暴 -10.2129 ▁室 -10.2129 叔 -10.2132 ▁公园 -10.2132 ▁自信 -10.2142 ▁兵 -10.2142 ▁凤 -10.2144 ▁霍 -10.2148 ▁一身 -10.2149 ▁业绩 -10.2151 ▁服装 -10.2153 藏 -10.2154 ▁狼 -10.2155 ▁高中 -10.2161 ▁of -10.2165 ▁每日 -10.2166 ▁三人 -10.2166 ▁一面 -10.2169 ▁配件 -10.2171 ▁原创 -10.2173 ▁便宜 -10.2181 露 -10.2183 ▁各自 -10.2184 ▁Pr -10.2186 罩 -10.2187 ▁优先 -10.2187 ▁标志 -10.2193 烟 -10.2195 ▁深化 -10.2201 杆 -10.2204 ▁威 -10.2204 ▁帝 -10.2206 ▁骂 -10.2207 ▁一半 -10.2207 ▁混合 -10.2208 ▁铺 -10.221 ▁上下 -10.2211 ▁略 -10.2211 屾 -10.2213 ▁隐私 -10.2215 患 -10.2217 ▁移 -10.2218 ▁静 -10.2219 ▁车型 -10.222 ▁信用 -10.2229 ▁适当 -10.2234 ▁动态 -10.2239 ▁隔 -10.224 ▁非法 -10.224 ▁尝试 -10.2242 ▁弱 -10.2243 ▁浣 -10.2245 ▁迎来 -10.2249 ▁应对 -10.2251 ▁维 -10.2255 ▁喂 -10.2257 ▁添加 -10.226 ▁标题 -10.2268 ▁小小 -10.2272 ▁紧紧 -10.2274 十 -10.229 ▁废 -10.2294 ▁瞧 -10.23 ▁认可 -10.2302 ▁辅助 -10.2304 ▁身份证 -10.2313 ▁分分 -10.2314 ▁累计 -10.2314 季度 -10.2318 ▁转移 -10.2322 ▁手上 -10.2326 ▁范 -10.2327 郎 -10.2328 ▁账户 -10.2334 涛 -10.2336 裂 -10.2339 ▁话题 -10.234 补 -10.234 ▁思维 -10.2341 ▁董事长 -10.2344 ▁青春 -10.2345 ▁竹 -10.2363 ▁信任 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-12.3931 ▁自考 -12.3933 枕 -12.3933 dd -12.3933 咳 -12.3934 方便 -12.3936 ▁姹 -12.3936 訊 -12.3936 hu -12.3937 ▁驻地 -12.3937 ▁CBA -12.3938 ▁新风 -12.394 呮 -12.394 ▁惰 -12.394 ure -12.3941 ▁希尔 -12.3942 ▁升华 -12.3942 ▁烤肉 -12.3943 ╂ -12.3943 ▁帷幕 -12.3943 ▁赞誉 -12.3945 肿瘤 -12.3946 喳 -12.3946 ▁武学 -12.3948 ▁追加 -12.395 ▁比尔 -12.395 緢 -12.3951 巨 -12.3952 ▁第十七 -12.3953 ans -12.3956 ▁皂 -12.3956 ▁洋溢 -12.3957 St -12.3957 ▁文凭 -12.396 ▁大字 -12.3962 ▁从根本 -12.3966 ▁颂 -12.3967 ▁同名 -12.3967 ▁贫血 -12.3968 難 -12.3969 ▁务工 -12.3969 ▁CA -12.397 ▁衡阳 -12.3972 net -12.3972 cr -12.3973 淨 -12.3974 ▁动画片 -12.3977 ▁吸毒 -12.3977 ▁专家学 -12.3977 ▁感官 -12.3978 ▁230 -12.3978 ▁特工 -12.3978 ▁更长 -12.3978 ▁恩怨 -12.3979 ▁更衣 -12.398 ▁兴业 -12.398 ▁防滑 -12.3981 ▁扩充 -12.3982 ▁最能 -12.3982 盯 -12.3983 橙 -12.3983 ▁短路 -12.3984 ▁点火 -12.3984 ▁儒家 -12.3984 梭 -12.3985 tu -12.3989 ▁Linux -12.3989 ▁品德 -12.399 巅 -12.3991 sy -12.3992 ▁钦 -12.3992 ▁不差 -12.3992 ≤ -12.3992 ▁柔声 -12.3993 恃 -12.3993 一亮 -12.3994 ▁狂热 -12.3999 ▁参议 -12.4 ▁化石 -12.4001 鑸 -12.4001 烬 -12.4002 ▁买来 -12.4004 ▁白金 -12.4006 ▁悉尼 -12.4006 眼圈 -12.4006 谎 -12.4006 ▁全国人大常委会 -12.4007 ▁农作 -12.4008 囱 -12.4009 ▁結構 -12.4009 ▁寒意 -12.401 ▁末日 -12.401 ▁老老 -12.4012 ▁乐视 -12.4013 ▁管事 -12.4014 ▁娑 -12.4016 ▁原生 -12.4016 ▁争执 -12.4016 ▁登出 -12.4017 ▁瞳 -12.4017 IT -12.4018 祭 -12.4018 ▁安阳 -12.4019 ▁雪地 -12.4019 ▁纳兰 -12.4019 煦 -12.402 1.0 -12.4022 ▁TA -12.4022 bi -12.4022 RI -12.4023 ▁朋 -12.4024 ▁破门 -12.4024 CT -12.4025 UT -12.4026 ╃ -12.4027 ▁盘算 -12.4028 ▁直营 -12.403 ▁车友 -12.4032 ▁麻痹 -12.4032 ▁白骨 -12.4034 ▁MO -12.4034 淇 -12.4035 ▁山中 -12.4036 ▁离合 -12.4036 ▁皮鞋 -12.4037 ▁CH -12.4039 ▁裘 -12.404 ▁负有 -12.404 刚落 -12.4042 腐败 -12.4044 怔 -12.4044 聞 -12.4045 ▁波斯 -12.4049 ▁iPad -12.4049 ▁Al -12.4049 进步 -12.4049 ▁加成 -12.405 ▁软硬 -12.405 ▁物理学 -12.405 ▁知己 -12.4052 ▁蓝光 -12.4052 ▁音樂 -12.4053 ▁报关 -12.4053 〗 -12.4053 钮 -12.4054 ▁推崇 -12.4056 ▁吃苦耐劳 -12.4056 ▁书架 -12.4057 ▁心智 -12.4058 ▁經過 -12.4058 ▁几只 -12.4058 ▁定下 -12.406 ▁群里 -12.4061 ▁有余 -12.4061 ▁美甲 -12.4066 ▁拖鞋 -12.4066 ▁公车 -12.4067 ▁筆 -12.4067 武帝 -12.4068 ▁破绽 -12.4069 ▁谦虚 -12.407 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-12.5198 ▁用尽 -12.5199 南京市 -12.5199 ▁Cookie -12.5202 ▁靓丽 -12.5202 ▁狠抓 -12.5202 ▁网约 -12.5203 ▁叱 -12.5204 ▁年版 -12.5204 进行 -12.5205 ▁芊 -12.5205 策划 -12.5208 ▁相隔 -12.521 ▁血统 -12.521 ▁浦东 -12.521 慶 -12.5211 釋 -12.5212 涚 -12.5212 ▁电光 -12.5213 ▁各路 -12.5215 纳斯 -12.5216 ▁联军 -12.5217 不掉 -12.5218 ▁贯通 -12.5218 ▁忐忑 -12.5218 ▁飲 -12.522 ▁侵袭 -12.522 ▁炕 -12.522 ▁接听 -12.5221 ▁属地 -12.5221 ▁燥 -12.5222 ▁冲到 -12.5223 粤 -12.5223 ▁201 -12.5223 ▁千秋 -12.5224 ▁嫣 -12.5225 多时 -12.5226 ▁剥离 -12.5227 ▁乳液 -12.5228 江湖 -12.5232 ▁闪避 -12.5233 ▁轻视 -12.5233 ▁初恋 -12.5233 ▁科技部 -12.5234 ▁首轮 -12.5236 ▁瞩目 -12.5237 ▁竞彩 -12.5239 ▁版主 -12.524 ▁PO -12.5241 ▁找人 -12.5243 ▁沃尔沃 -12.5244 ▁汤姆 -12.5244 ▁献给 -12.5245 ▁計 -12.5245 ▁PET -12.5246 ▁IBM -12.5247 ▁紧身 -12.5249 戮 -12.5249 ▁卦 -12.525 ▁惡 -12.5252 ▁石门 -12.5253 ▁乐山 -12.5254 ▁布衣 -12.5254 ▁多想 -12.5255 ▁余下 -12.5255 ▁魔幻 -12.5256 拉扯 -12.5258 饰品 -12.5258 ▁古巴 -12.5261 ▁排骨 -12.5264 ▁瘫痪 -12.5264 ▁多彩 -12.5265 ▁殴打 -12.5266 ▁凹凸 -12.5266 晓明 -12.5267 ▁计数 -12.5267 理解 -12.5267 ▁意見 -12.5267 女儿 -12.5268 ▁亢 -12.527 ▁捡起 -12.527 峥 -12.5271 ▁水肿 -12.5273 ▁同城 -12.5273 而上 -12.5274 ▁推断 -12.5274 ▁嬴 -12.5274 ▁谨防 -12.5275 ▁六七 -12.5275 ▁夙 -12.5276 肽 -12.5276 ▁遗留 -12.5276 ▁底薪 -12.5277 ≥ -12.5277 ▁揩 -12.5279 ▁SM -12.5281 ▁淡化 -12.5282 ID -12.5283 ▁PA -12.5284 ▁反腐 -12.5284 ▁妇产 -12.5284 肋 -12.5286 ▁武当 -12.5287 ▁榆 -12.529 花园 -12.529 ▁内核 -12.529 ▁考评 -12.5291 良好 -12.5291 ▁并列 -12.5291 .95 -12.5292 ▁跟我 -12.5294 針 -12.5295 ▁极乐 -12.5298 ▁發布 -12.5298 ▁三步 -12.53 前进 -12.53 ▁霹雳 -12.53 ▁海淀 -12.53 巫 -12.53 ▁去向 -12.5301 燒 -12.5302 ▁波及 -12.5303 ▁拉丁 -12.5305 ▁虚幻 -12.5305 ▁亚军 -12.5305 ▁肇庆 -12.5306 思路 -12.5309 ▁私营 -12.5311 ▁風險 -12.5311 婕 -12.5312 寄 -12.5313 ▁估价 -12.5315 旀 -12.5315 ▁房中 -12.5315 ▁单手 -12.5315 VE -12.5315 ▁劳累 -12.5316 驗 -12.5316 ▁灵性 -12.5316 ▁中所 -12.5317 ▁卫东 -12.5317 怯 -12.5317 ▁杨浩 -12.5318 ▁辦理 -12.5318 联播 -12.5318 ▁才刚 -12.5318 ▁学徒 -12.532 濋 -12.532 ▁还敢 -12.5321 ▁嚴 -12.5321 ▁史蒂 -12.5322 ▁从小到 -12.5322 ▁处份 -12.5322 ▁吸尘 -12.5323 蒸 -12.5323 ▁覺得 -12.5327 ▁苍蝇 -12.5327 枯 -12.5329 毒药 -12.5329 ▁電視 -12.533 ▁瞿 -12.533 ▁相等 -12.5331 ▁史诗 -12.5333 ▁189 -12.5335 ▁旧金山 -12.5337 ▁可得 -12.534 改造 -12.5342 ▁流下 -12.5343 ▁绒 -12.5344 ▁商报 -12.5346 ▁今朝 -12.5346 ▁七大 -12.5348 ▁2030 -12.5348 ▁疲倦 -12.5349 ▁震天 -12.5349 ▁盔 -12.5351 护航 -12.5353 ▁虔诚 -12.5353 ▁惊吓 -12.5354 ▁中国银行 -12.5355 ▁Ar -12.5357 ▁萧晨 -12.5357 ▁怎麼 -12.5358 ▁波浪 -12.5359 鏡 -12.5359 魰 -12.5359 ▁自查 -12.5361 龙女 -12.5363 ▁飞奔 -12.5363 ▁研發 -12.5364 浸 -12.5365 ▁造船 -12.5366 揭 -12.5366 ▁单调 -12.5368 ▁咪 -12.5369 ▁螃蟹 -12.537 ▁仇人 -12.5371 汶 -12.5372 ▁人要 -12.5372 阅读 -12.5372 ▁短片 -12.5373 ▁儿科 -12.5375 ▁腮 -12.5376 冤 -12.5377 ▁SH -12.538 ▁冰川 -12.538 量化 -12.538 ▁白皮 -12.538 米尔 -12.5381 庤 -12.5381 ▁液位 -12.5381 歆 -12.5384 ▁钾 -12.5384 ▁舍利 -12.5384 ▁空心 -12.5384 ▁港澳台 -12.5385 岋 -12.5386 ▁立委 -12.5386 ▁梭 -12.5387 ▁软软 -12.5388 茅 -12.5389 ▁应邀 -12.539 120 -12.539 ▁李明 -12.5392 ▁驚 -12.5392 ▁土工 -12.5393 ▁宣誓 -12.5394 ▁一文 -12.5395 时许 -12.5398 ▁挂断 -12.5398 ▁听证 -12.5399 丫 -12.54 ▁棱 -12.5401 ▁2.8 -12.5401 ▁圆圆 -12.5403 ▁尼斯 -12.5403 ▁生平 -12.5404 配合 -12.5405 ▁书信 -12.5407 ▁一个劲 -12.5408 ▁超碰 -12.5409 ▁军区 -12.5409 コ -12.5411 ▁居士 -12.5411 ▁热处理 -12.5412 ▁刪除 -12.5413 ▁天界 -12.5414 530 -12.5415 ▁捷克 -12.5416 ide -12.5416 纳河 -12.5417 ▁通州 -12.5418 崄 -12.5419 ▁从头到 -12.542 ▁滞后 -12.5421 ▁悲痛 -12.5421 ▁低低 -12.5422 保障 -12.5422 ▁进水 -12.5423 ▁元神 -12.5424 短信 -12.5426 ▁洽 -12.5426 ▁由衷 -12.5427 饺 -12.5429 ▁弱小 -12.543 ▁慵 -12.5432 ▁切身 -12.5434 本科 -12.5434 ▁西域 -12.5434 ▁坝 -12.5434 ▁增至 -12.5435 ▁懦 -12.5436 ▁罗汉 -12.5436 320 -12.5437 ▁走红 -12.5437 ▁欒 -12.5438 ▁写信 -12.5439 ▁绊 -12.544 ↑ -12.5441 ▁若能 -12.5442 騗 -12.5443 ▁坦白 -12.5444 ▁衙 -12.5445 ▁木马 -12.5446 ▁诚心 -12.5446 ▁启蒙 -12.5447 ▁马蹄 -12.5447 ▁杜承 -12.5448 ▁环保型 -12.5448 ▁盾牌 -12.5449 ▁五万 -12.545 墩 -12.5451 ▁青蛙 -12.5451 铏 -12.5452 ▁接班 -12.5455 溂 -12.5455 ▁瞬息 -12.5456 ▁卷帘 -12.5456 ▁天佑 -12.5457 ▁大清 -12.5458 ▁花儿 -12.5459 ▁退后 -12.5459 ▁咬着 -12.546 ste -12.5462 ▁满身 -12.5462 ▁奉行 -12.5462 ▁同居 -12.5462 ▁喝醉 -12.5463 ▁藥 -12.5464 ence -12.5465 蜒 -12.5466 ▁檢 -12.5466 ▁殿堂 -12.5467 ▁四点 -12.5467 ▁盐酸 -12.5467 ▁保税 -12.5468 ▁轻巧 -12.5469 ▁民政 -12.547 VA -12.5471 ▁洗发 -12.5472 后果 -12.5473 ▁概不 -12.5473 ▁或多 -12.5475 ▁海滨 -12.5476 ▁杉 -12.5477 ▁专车 -12.5482 ▁声势 -12.5482 ▁臉 -12.5483 ▁阿森纳 -12.5483 ▁车内 -12.5483 烷 -12.5483 ▁討論 -12.5484 ▁红颜 -12.5484 ▁丈 -12.5485 56 -12.5487 ▁天雷 -12.5487 ▁环卫 -12.5487 ▁电量 -12.5488 ▁SD -12.5489 ▁岚 -12.5489 ▁音量 -12.549 産 -12.5491 my -12.5491 ▁挑逗 -12.5491 ▁幻觉 -12.5492 ▁卫士 -12.5492 ▁裂纹 -12.5494 ▁农业部 -12.5495 屡 -12.5495 ▁临走 -12.5498 ▁la -12.5498 ▁远超 -12.5499 ▁贬 -12.5499 ▁重叠 -12.5501 霉素 -12.5501 ▁清冷 -12.5501 ▁零售价 -12.5501 ▁欣然 -12.5501 ▁183 -12.5501 ▁车床 -12.5502 ▁中要 -12.5503 按 -12.5503 ▁讀 -12.5505 ▁救出 -12.5506 ▁显露 -12.5508 ▁兴旺 -12.5508 ▁之子 -12.5509 ▁德育 -12.551 ▁通通 -12.551 ▁快到 -12.5511 ▁大马 -12.5512 ▁皖 -12.5514 ▁玩得 -12.5515 塑料 -12.5515 ▁催生 -12.5516 ▁快车 -12.5516 ▁玩儿 -12.5516 ▁建构 -12.5517 ▁几道 -12.5518 ▁林木 -12.5518 ▁NT -12.5518 同于 -12.5519 ns -12.5521 ▁收银 -12.5521 ▁刺痛 -12.5521 ▁补习 -12.5521 ▁特制 -12.5521 ▁煲 -12.5523 ▁护送 -12.5523 ▁变相 -12.5523 维斯 -12.5525 ▁禁毒 -12.5525 ▁担负 -12.5525 冒 -12.5525 ▁含税 -12.5525 ▁刻画 -12.5526 ▁深感 -12.5526 ▁新郎 -12.553 ▁口子 -12.553 ▁摆摆 -12.5531 ▁软化 -12.5532 ▁亲热 -12.5533 ▁确有 -12.5535 ran -12.5536 ▁泰州 -12.5537 84.23 -12.5537 鐵 -12.5538 松松 -12.5538 ▁席位 -12.5538 iz -12.5538 ▁危急 -12.5538 ▁呕 -12.5538 ▁壓 -12.5539 自治县 -12.5539 芊 -12.554 ▁陰 -12.5541 ▁皇城 -12.5542 ▁參考 -12.5544 ▁淫水 -12.5544 ▁尘土 -12.5544 ▁艳丽 -12.5544 茸 -12.5545 ▁平地 -12.5545 仪表 -12.5545 ▁表妹 -12.5545 ▁爱国主义 -12.5546 ▁选秀 -12.5546 ▁十来 -12.5546 ▁法语 -12.5547 ▁漠 -12.5548 mi -12.5548 ▁国美 -12.5549 ▁迷迷 -12.5551 ▁发泡 -12.5552 ▁石榴 -12.5552 日游 -12.5553 咎 -12.5553 ▁冊 -12.5553 晔 -12.5554 ▁除湿 -12.5554 ▁英勇 -12.5555 ▁安全事故 -12.5555 ▁多头 -12.5556 ▁小姨 -12.5557 ▁16774 -12.5558 ▁收下 -12.5559 ▁坍 -12.5559 黨 -12.556 ▁慷慨 -12.556 ▁提款 -12.556 ▁聊城 -12.5561 91 -12.5562 ▁不分 -12.5562 ▁集市 -12.5563 ▁过长 -12.5563 ▁天灾 -12.5567 ▁朴素 -12.5568 天盖 -12.5568 1.5 -12.5568 蚕 -12.5569 ▁时日 -12.5569 ▁厦门市 -12.5569 囩 -12.5571 博物馆 -12.5574 ▁ICP -12.5576 ▁磁盘 -12.5576 ▁匠心 -12.5577 ▁退伍 -12.5577 ▁战火 -12.5578 ▁纯真 -12.5582 ▁甜品 -12.5582 ▁碍 -12.5583 ▁存货 -12.5583 ▁来宾 -12.5584 IL -12.5584 ▁林立 -12.5585 ▁沼泽 -12.5587 ▁流云 -12.5588 舉 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▁撑起 -12.6467 ▁发源 -12.6467 ▁等离子 -12.6468 ▁榫 -12.6469 ▁前排 -12.647 ▁校准 -12.647 ▁日历 -12.647 ▁改變 -12.6471 羹 -12.6471 ▁通体 -12.6472 ▁黑色素 -12.6472 way -12.6473 ▁歌剧 -12.6473 ▁锋芒 -12.6473 适应 -12.6474 ▁非标 -12.6474 ▁彈 -12.6474 貴 -12.6475 ▁低保 -12.6476 ▁化疗 -12.6477 ▁拉链 -12.6477 叙 -12.6478 ▁vi -12.648 微信 -12.648 ▁per -12.6481 ▁婚庆 -12.6482 ▁王林 -12.6482 强大 -12.6482 ▁狀況 -12.6482 ▁足彩 -12.6483 辊 -12.6484 ▁意气 -12.6484 瞰 -12.6485 願 -12.6485 挟 -12.6486 ▁太低 -12.6488 ▁拜师 -12.6488 ▁絕 -12.6488 ▁因地制 -12.6488 ast -12.649 ▁自然科学 -12.6491 ▁提质 -12.6492 少侠 -12.6493 ▁地雷 -12.6493 ▁秀丽 -12.6494 ▁恶毒 -12.6494 ▁惦记 -12.6495 ▁褒 -12.6496 ▁受灾 -12.6497 ▁称得 -12.6498 ▁哪一 -12.6499 ▁大洋 -12.6499 ▁义工 -12.6499 評 -12.6499 ▁正气 -12.65 会员 -12.65 ▁辛辣 -12.65 ▁TD -12.6501 萃 -12.6501 ▁如风 -12.6502 ▁胭脂 -12.6503 ▁---- -12.6504 ▁心力 -12.6506 ▁京剧 -12.6506 正恩 -12.6507 ▁热忱 -12.6507 ▁令狐冲 -12.651 朔 -12.6512 令营 -12.6512 ▁求救 -12.6513 ▁裁量 -12.6514 ▁申博 -12.6514 沖 -12.6515 ▁觉察 -12.6515 ▁结交 -12.6516 ▁暂且 -12.6517 ▁蝉 -12.6518 ▁冒犯 -12.6518 ▁地底 -12.6518 祖国 -12.6519 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▁span -12.8274 ▁学着 -12.8274 ▁夏利 -12.8274 ▁魔气 -12.8274 ▁受让 -12.8275 ▁袁绍 -12.8275 ▁很早 -12.8275 拆 -12.8276 ▁结石 -12.8277 ▁ч -12.8278 MW -12.8279 ▁部門 -12.8279 钼 -12.828 ▁领航 -12.8281 ▁中风 -12.8281 ▁出生地 -12.8282 ▁敦促 -12.8282 ▁露营 -12.8282 ▁290 -12.8283 ▁隔天 -12.8283 视镜 -12.8284 ▁不绝 -12.8285 酰胺 -12.8285 ▁滑稽 -12.8286 ▁变身 -12.8286 ▁黑板 -12.8287 系統 -12.8289 ▁轻叹 -12.8289 ▁阴险 -12.8289 丽莎 -12.8289 禧 -12.829 ▁避难 -12.829 ▁木瓜 -12.829 ▁醒过 -12.8291 ▁容错 -12.8291 ▁輪 -12.8291 当当 -12.8292 解释 -12.8294 7.4 -12.8295 ▁伯格 -12.8295 ▁摇篮 -12.8296 ▁下级 -12.8296 ▁肖像 -12.8297 ▁145 -12.8297 out -12.8298 賞 -12.8299 ▁同质 -12.8299 ▁韶关 -12.8299 uc -12.8301 ▁鼻梁 -12.8303 ▁汶 -12.8303 ▁械 -12.8304 ▁多米 -12.8304 ▁野战 -12.8304 ▁健脾 -12.8308 ▁遮盖 -12.8308 ▁举个 -12.8308 ▁地市 -12.8309 ▁万美 -12.831 ▁台独 -12.831 eta -12.831 ▁km -12.831 峪 -12.8311 偉 -12.8314 lic -12.8314 ▁一脉 -12.8315 ▁巳 -12.8315 ▁着火 -12.8316 ▁洗护 -12.8318 鍕 -12.8318 ▁Internet -12.832 ▁羽翼 -12.832 und -12.8322 .66 -12.8325 ▁悄无 -12.8326 ▁鲨 -12.8327 ▁Su -12.8327 ▁蛙 -12.8327 ▁雷鸣 -12.8328 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▁台前 -13.1886 ▁階段 -13.1886 梅尔 -13.1887 ▁重合 -13.1887 ▁铃铛 -13.1888 ▁手袋 -13.1888 mar -13.1888 ▁二氧化 -13.1888 ▁地税 -13.189 ▁练兵 -13.189 ▁尸骨 -13.1891 ▁龙潭 -13.1891 ▁逆行 -13.1891 ▁機制 -13.1892 ▁古文 -13.1892 ▁再接再 -13.1892 之徒 -13.1893 4.8 -13.1893 ▁众将 -13.1893 湍 -13.1893 ▁經典 -13.1893 ▁危房 -13.1894 国务院 -13.1895 鲤 -13.1896 ▁春暖花 -13.1896 6.2 -13.1898 ▁成員 -13.1899 忿 -13.19 ▁车用 -13.19 跡 -13.19 ▁训练场 -13.19 ▁施政 -13.1901 490 -13.1901 ▁性病 -13.1902 挨 -13.1902 ▁運行 -13.1902 汞 -13.1903 ▁得不偿失 -13.1906 ▁供图 -13.1906 ▁相间 -13.1907 ▁依着 -13.1907 垰 -13.1908 ▁233 -13.1909 ▁假象 -13.1909 辫 -13.1911 ▁現象 -13.1911 ▁思忖 -13.1912 一凡 -13.1912 ▁摩托罗拉 -13.1912 荟 -13.1913 ▁九寨沟 -13.1915 ▁国际象棋 -13.1915 rp -13.1916 ▁三秒 -13.1918 不錯 -13.1918 ▁Office -13.1918 ▁胎盘 -13.1918 ▁走遍 -13.1919 悯 -13.1922 ▁澹台 -13.1923 ▁內部 -13.1923 如山 -13.1926 突出 -13.1926 ▁结肠 -13.1927 ▁Sun -13.1927 ▁云林 -13.1927 ▁萨克 -13.1927 ▁河畔 -13.1928 风寒 -13.1928 俑 -13.1929 悄悄 -13.1929 ▁尘世 -13.1929 ▁198 -13.1929 场次 -13.1929 ▁压铸 -13.1929 余悸 -13.193 段落 -13.193 掖 -13.193 结局 -13.1931 7.2 -13.1931 ▁献身 -13.1931 ▁条理 -13.1933 晚饭 -13.1934 ▁躲藏 -13.1934 ▁TXT -13.1934 ▁城东 -13.1935 凈 -13.1935 kk -13.1935 ▁农副 -13.1936 .70 -13.1937 ▁颓废 -13.1937 飞燕 -13.1937 ▁吱吱 -13.1939 Net -13.194 彝 -13.1941 ▁漂泊 -13.1942 ▁脖 -13.1942 ▁简简 -13.1943 ▁實性 -13.1946 ▁精兵 -13.1949 ▁生硬 -13.1949 GI -13.195 支队 -13.1951 ▁青丝 -13.1951 ▁延庆 -13.1952 ▁崎岖 -13.1953 ▁花旗 -13.1954 ▁腹胀 -13.1955 ▁爱看 -13.1958 ▁诸法 -13.196 ▁见得 -13.196 ▁装订 -13.196 ▁黑山 -13.1961 ▁取景 -13.1961 ▁科举 -13.1963 ▁達到 -13.1965 悠扬 -13.1965 ▁变大 -13.1965 ▁白内 -13.1966 ▁習慣 -13.1968 盂 -13.1968 425 -13.1968 ▁8.2 -13.1968 ▁收容 -13.1971 ▁吸音 -13.1971 455 -13.1972 大桥 -13.1973 ▁恼羞成 -13.1974 ▁除臭 -13.1975 ▁李佳 -13.1975 技術 -13.1975 920 -13.1975 ▁编导 -13.1976 ▁蝗 -13.1976 ▁国库 -13.1976 ▁城西 -13.1977 Ch -13.1977 榴 -13.1977 ▁漂移 -13.1977 貼 -13.198 ▁先给 -13.198 獨 -13.1981 ≡ -13.1981 ▁乌兰 -13.1981 ▁实况 -13.1982 軟 -13.1982 ▁拳脚 -13.1983 ▁绝情 -13.1984 ▁王八 -13.1984 ▁维稳 -13.1986 ▁有损 -13.1986 ors -13.1988 ▁真實 -13.1988 褂 -13.1988 礼品 -13.1989 850 -13.1989 ▁白龙 -13.199 氨 -13.1991 ▁皓月 -13.1991 ▁磨砂 -13.1991 ▁静坐 -13.1992 ▁龙井 -13.1993 ▁下唇 -13.1993 ▁病害 -13.1993 ▁淮北 -13.1995 ▁斗罗 -13.1997 ▁目不转 -13.1998 ▁一男一 -13.1998 ▁小萝莉 -13.1998 ▁一松 -13.1999 ▁球磨 -13.2 ▁麻花 -13.2 ▁哪吒 -13.2001 孫 -13.2002 宀 -13.2003 ▁損失 -13.2003 ▁除草 -13.2003 颍 -13.2005 ▁司马懿 -13.2005 ▁5500 -13.2005 ▁副驾驶 -13.2006 ▁大四 -13.2009 ▁阴雨 -13.2009 ▁云山 -13.201 ▁蛾 -13.2011 ▁功名 -13.2011 ▁酋长 -13.2011 飚 -13.2012 ▁取件 -13.2013 ▁细嫩 -13.2013 ▁龙腾 -13.2013 条不 -13.2014 生气 -13.2015 家电 -13.2015 ▁抿嘴 -13.2016 ▁白俄罗斯 -13.2017 ▁肉食 -13.2017 ▁改口 -13.2018 ▁南亚 -13.2018 ▁勃然 -13.2018 ▁微红 -13.202 ▁陆陆 -13.202 ▁衰减 -13.202 ▁消防车 -13.202 ▁贾跃亭 -13.2021 ▁米勒 -13.2021 隐私 -13.2022 ▁市容 -13.2023 ▁Mb -13.2024 ▁异口同 -13.2024 ▁苏瑾 -13.2025 ral -13.2026 ▁豆类 -13.2026 ▁婉转 -13.2027 ▁癫 -13.2027 ▁醫師 -13.2028 ▁掌教 -13.2028 ▁仁爱 -13.2029 ▁豪杰 -13.2029 ▁娇媚 -13.2029 ▁一塌糊 -13.203 ▁西证 -13.203 ▁结节 -13.2031 一波 -13.2031 ▁花盆 -13.2031 ▁黑水 -13.2032 ▁叶尘 -13.2032 ▁铁甲 -13.2035 区划 -13.2036 ▁惨淡 -13.2037 ▁依序 -13.2037 ▁稳中 -13.2037 ▁You -13.2037 ▁披肩 -13.2038 ▁显赫 -13.2038 践 -13.204 業者 -13.2041 ▁娄底 -13.2041 ▁随笔 -13.2041 ▁懵懂 -13.2043 万两 -13.2043 ▁制式 -13.2044 ▁狗屁 -13.2045 ▁草酸 -13.2047 ▁电影票 -13.2048 詋 -13.2048 ▁昼 -13.2048 ▁情急 -13.2049 ▁整流 -13.2049 TU -13.205 典型 -13.2051 304 -13.2051 拘 -13.2052 ▁六千 -13.2052 ▁氦 -13.2052 ▁松口 -13.2053 ▁意欲 -13.2054 锂 -13.2055 绞 -13.2057 ▁喜讯 -13.2058 ▁拘束 -13.206 ▁左上 -13.206 ▁聆 -13.2062 幸福 -13.2062 ▁进料 -13.2062 ▁造物 -13.2063 330 -13.2063 ▁擂 -13.2065 ▁声乐 -13.2065 外交部 -13.2067 玩具 -13.2067 ▁楹 -13.2068 ▁掣 -13.2073 ▁Fe -13.2074 ▁顽皮 -13.2075 ▁意犹未 -13.2075 ▁红莲 -13.2075 ▁定睛 -13.2076 ▁蒙蒙 -13.2076 ▁银山 -13.2076 ▁3200 -13.2076 米兰 -13.2076 粟 -13.2076 ▁辨析 -13.2076 訓 -13.2077 ▁時尚 -13.2077 光大 -13.2077 box -13.2078 祐 -13.2078 sf -13.2078 ▁助剂 -13.2079 ▁早产 -13.2079 裱 -13.208 浪费 -13.2082 风发 -13.2082 ▁兴隆 -13.2082 118 -13.2083 唆 -13.2083 ▁鲤 -13.2084 竻 -13.2084 ▁病毒感染 -13.2085 908 -13.2086 ▁储气 -13.2086 ▁成婚 -13.2086 牆 -13.2087 sch -13.2087 525 -13.2088 ▁成瘾 -13.2088 ▁半夏 -13.2088 ▁傲天 -13.2089 ▁传令 -13.2089 ▁难求 -13.209 ous -13.2091 ▁接住 -13.2091 屏幕 -13.2091 扈 -13.2092 ▁1954 -13.2093 ▁捕捞 -13.2093 志刚 -13.2093 ▁二零一 -13.2094 ▁2700 -13.2094 ▁肝胆 -13.2094 ▁非农 -13.2094 ▁一肖中 -13.2094 公室 -13.2096 ▁富民 -13.2097 ▁素颜 -13.2098 ▁瞪眼 -13.2099 ▁诵读 -13.21 益匪浅 -13.21 辘 -13.2101 ▁航站 -13.2103 1.1 -13.2103 ▁葫芦岛 -13.2104 ▁易购 -13.2104 ▁张杰 -13.2104 ▁火电 -13.2105 飛 -13.2105 ▁俘 -13.2106 ▁赠与 -13.2107 ▁伯恩 -13.2108 万计 -13.2109 ▁完好无 -13.211 ▁真够 -13.2112 ▁掘 -13.2113 高考 -13.2113 ▁紫气 -13.2114 ▁印第安 -13.2114 ▁萎 -13.2115 rea -13.2116 反射 -13.2117 ▁交了 -13.2117 ▁富家 -13.2119 699 -13.2119 蔷 -13.2119 ▁宜兴 -13.2119 ▁染发 -13.212 ▁南疆 -13.212 sen -13.212 纷纷 -13.2124 ▁闲话 -13.2124 蒙蒙 -13.2126 氰 -13.2127 ▁山间 -13.2128 ▁覆灭 -13.2128 ▁巴勒斯坦 -13.2129 ▁三叔 -13.2129 235 -13.213 独秀 -13.213 ▁威斯 -13.2131 ▁FB -13.2132 ▁偏重 -13.2132 ▁三段 -13.2133 ▁SW -13.2133 ▁少先队 -13.2134 ▁铂金 -13.2134 ▁五颜六 -13.2135 ▁全封闭 -13.2135 ▁炮火 -13.2135 ▁帝国主义 -13.2137 老四 -13.2137 ▁169 -13.2137 ▁脱手 -13.2137 喜事 -13.2138 ▁城下 -13.2138 ▁埋藏 -13.2139 而无 -13.214 党史 -13.214 men -13.2142 ▁沦落 -13.2142 ▁扎堆 -13.2142 ▁相接 -13.2142 ▁散装 -13.2143 ▁嚦 -13.2143 rad -13.2144 ▁陈云 -13.2144 ▁义气 -13.2144 ▁闯荡 -13.2145 ▁之功 -13.2145 ▁哗啦 -13.2147 ▁寄来 -13.2147 ▁云阳 -13.2148 老虎 -13.2148 ▁果不其 -13.2148 指南 -13.2149 ▁style -13.2151 heng -13.2151 ▁冷却水 -13.2151 ▁无明 -13.2152 桃源 -13.2152 UM -13.2154 视眈 -13.2154 ▁满口 -13.2155 ▁文化名 -13.2155 书店 -13.2155 ▁探亲 -13.2157 忻 -13.2158 ▁MPV -13.2158 ▁弹奏 -13.2159 ▁发狂 -13.2159 ▁萧条 -13.216 tom -13.216 ▁正以 -13.2161 YS -13.2161 ▁鹿晗 -13.2161 女生 -13.2163 ▁读过 -13.2165 ▁美麗 -13.2165 悅 -13.2166 ▁绢 -13.2166 倩倩 -13.2166 ▁RPG -13.2167 670 -13.2167 ▁驻马店 -13.2169 ▁门卫 -13.2169 ▁查尔斯 -13.217 烯 -13.2171 ▁承诺书 -13.2171 配比 -13.2171 ▁议价 -13.2172 ▁太深 -13.2174 ▁密钥 -13.2176 ▁寻宝 -13.2177 装周 -13.2177 ▁has -13.2177 ▁照常 -13.2177 ▁三联 -13.2178 ▁闪躲 -13.2178 ▁读音 -13.2179 亰 -13.2179 ▁硬朗 -13.2179 ▁泡茶 -13.218 par -13.218 白白 -13.218 ▁判别 -13.218 ▁流着 -13.2182 ▁无踪 -13.2182 銅 -13.2183 ▁水压 -13.2183 ▁碧玉 -13.2184 ▁告一 -13.2184 ▁槟榔 -13.2185 推荐 -13.2185 478 -13.2185 昼 -13.2187 ▁翱翔 -13.2187 ▁次第 -13.2188 ▁乔楠 -13.2189 一级 -13.2189 自负 -13.2191 凶恶 -13.2191 6.7 -13.2192 嗲 -13.2193 ▁炒菜 -13.2193 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-13.5075 ▁菱形 -13.5075 ▁居心 -13.5075 ▁ARM -13.5075 ▁佩奇 -13.5076 趙 -13.5076 ▁造影 -13.5077 ▁西昌 -13.5078 ▁崇明 -13.5079 ▁罢免 -13.5079 rin -13.5079 pal -13.5079 ▁长腿 -13.5083 ▁五洲 -13.5084 ▁敲诈 -13.5084 nor -13.5084 一夕 -13.5085 ▁腥味 -13.5086 913 -13.5086 ▁青鸾 -13.5087 狩 -13.5087 ▁谦逊 -13.5087 驯 -13.5087 审查 -13.5088 2.3 -13.5088 襲 -13.5088 ▁闭塞 -13.5089 ▁结转 -13.509 ▁消防队 -13.5091 ▁牌型 -13.5092 ▁贴息 -13.5092 ▁天秤 -13.5093 ▁奥拉 -13.5095 mil -13.5096 ▁第三十六 -13.5096 遢 -13.5096 916 -13.5098 ▁睡觉时 -13.5099 ▁傷害 -13.5099 簯 -13.51 ▁珠光 -13.5101 701 -13.5101 ▁体育用品 -13.5102 滋事 -13.5103 难免 -13.5103 ▁张弛 -13.5103 422 -13.5104 ▁墓葬 -13.5104 ▁倚天 -13.5106 ▁选聘 -13.5106 IQ -13.5108 ▁甩出 -13.5108 357 -13.5108 丰收 -13.5108 865 -13.5109 謀 -13.511 ▁正月初 -13.5111 ▁老僧 -13.5112 ▁兰博基 -13.5112 778 -13.5113 笃 -13.5113 惺 -13.5113 ▁西江 -13.5114 ▁开门见 -13.5115 ▁担负起 -13.5117 ▁炼金 -13.5117 暫 -13.5118 ▁打開 -13.5118 ▁竹叶 -13.5119 BR -13.5119 oth -13.5119 ▁读数 -13.5119 ▁军政 -13.5119 tory -13.5119 醬 -13.5121 ▁反潜 -13.5122 ▁仙王 -13.5124 ▁乔木 -13.5125 ▁墨绿 -13.5125 spec -13.5126 868 -13.5127 ump -13.5129 ▁消泡 -13.513 ▁Wind -13.513 ▁计程 -13.513 ▁施以 -13.513 ▁娇笑 -13.5131 ▁烧饼 -13.5131 ▁抚州 -13.5133 ▁继续加强 -13.5133 ▁姆巴 -13.5133 ▁山峦 -13.5134 ▁龙辰 -13.5134 ▁自欺 -13.5135 惩 -13.5135 ▁易碎 -13.5135 ▁刻录 -13.5135 ▁祝你 -13.5136 ▁血雾 -13.5137 ▁生津 -13.5137 ▁陈奕 -13.5138 ▁LPL -13.5139 萤 -13.514 ▁祝融 -13.5141 ▁绿意 -13.5142 ▁WEB -13.5142 基督 -13.5144 嗟 -13.5145 Mobile -13.5145 ▁单亲 -13.5145 ▁兴办 -13.5146 340 -13.5147 2016 -13.5147 ▁类药 -13.5148 ▁胸襟 -13.5148 999 -13.5148 ▁国务委员 -13.515 ▁根深蒂 -13.5151 噸 -13.5151 ▁胃部 -13.5152 漸 -13.5154 ▁语重心 -13.5154 ▁售中 -13.5154 〝 -13.5155 ▁娇声 -13.5155 ▁家俱 -13.5159 擀 -13.5159 不挂 -13.516 ▁連接 -13.516 ▁進步 -13.5161 ache -13.5161 ▁米粒 -13.5163 吊胆 -13.5164 ▁延吉 -13.5165 ▁洛离 -13.5165 ▁约瑟夫 -13.5166 ▁几杯 -13.5167 ▁刘枫 -13.5167 ▁存管 -13.5167 ▁united -13.517 ▁国产品牌 -13.517 ▁总领事 -13.5171 ▁颠覆性 -13.5172 货币基金 -13.5172 一刻 -13.5172 ▁李昊 -13.5173 aj -13.5173 UC -13.5176 磺 -13.5176 1.9 -13.5177 罗列 -13.5178 ▁指標 -13.518 差异 -13.5181 齐齐 -13.5184 祎 -13.5184 ▁艳阳 -13.5186 徕 -13.5186 ▁红霞 -13.5187 ridge -13.5187 ▁收跌 -13.5187 ▁OPEC -13.5187 ▁嗓门 -13.5188 呷 -13.5188 很 -13.5189 ▁锯齿 -13.5189 ration -13.519 ▁皮炎 -13.5191 完整 -13.5191 步枪 -13.5192 ▁关进 -13.5192 吴 -13.5192 ▁不同寻常 -13.5193 ▁汗毛 -13.5193 呛 -13.5194 核心 -13.5194 ▁法兰西 -13.5194 璜 -13.5195 渡河 -13.5195 plus -13.5196 ▁朝野 -13.5198 ▁汽水 -13.5198 佯 -13.5199 ▁三门峡 -13.5199 ▁莫急 -13.5199 ▁拼凑 -13.5199 ▁脚掌 -13.5199 ▁MSCI -13.52 bao -13.5201 ▁铜川 -13.5201 Service -13.5202 ▁扣留 -13.5202 ▁商標 -13.5203 ▁1933 -13.5203 蜃 -13.5204 杳 -13.5206 ▁龚瑞妮 -13.5207 ▁人山人 -13.5208 ▁水煮 -13.5208 ▁面红耳 -13.5208 ▁包揽 -13.5208 ▁从无到 -13.5209 198 -13.5209 ▁喷气 -13.5209 ▁致谢 -13.521 ▁南朝 -13.5211 划一 -13.5213 ▁發出 -13.5214 ▁目视 -13.5215 ▁翻譯 -13.5216 岫 -13.5217 ▁第五套 -13.5218 ▁夺命 -13.522 ▁闹市 -13.522 建工 -13.522 978 -13.5221 执法 -13.5222 ▁瑶瑶 -13.5223 ▁民情 -13.5223 助理 -13.5224 桉 -13.5227 硬盘 -13.5228 ▁爬虫 -13.5228 ▁德鲁 -13.5229 富贵 -13.5229 势所 -13.523 ▁觀光 -13.523 耶稣 -13.523 ▁卡巴斯基 -13.5231 ▁負擔 -13.5232 ▁卡耐 -13.5234 ries -13.5234 ▁仰面 -13.5234 楦 -13.5236 ▁力帆 -13.5237 Pad -13.5237 侮辱 -13.5238 恍惚 -13.524 ▁拔尖 -13.5242 ▁灰飞烟 -13.5243 117 -13.5244 ▁迷彩 -13.5244 ▁阿姆斯特 -13.5244 闠 -13.5244 ▁幽兰 -13.5244 ▁圆筒 -13.5244 ▁东道主 -13.5248 ▁问鼎 -13.5248 ▁农商 -13.5249 ▁一百六十 -13.525 park -13.525 煩 -13.5251 ▁收費 -13.5252 ▁枇杷 -13.5253 ▁树根 -13.5255 ▁定瓶 -13.5256 ▁阻滞 -13.5257 ▁互鉴 -13.5257 转印 -13.5257 ▁锅底 -13.526 醤 -13.5261 ▁狂飙 -13.5262 ▁趁热 -13.5263 ▁斩首 -13.5263 ▁绵羊 -13.5264 ▁撑开 -13.5264 ▁辍学 -13.5264 ▁陪你 -13.5264 order -13.5265 ▁蜂巢 -13.5265 ▁鹤壁 -13.5265 ▁晚些 -13.5266 878 -13.5267 美食 -13.5267 骋 -13.5268 ▁1927 -13.5269 677 -13.5269 ▁热乎 -13.5269 ▁脉动 -13.527 ▁試驗 -13.527 ▁酣畅 -13.5271 ▁瑞丽 -13.5273 4.6 -13.5273 推行 -13.5275 ▁檀香 -13.5276 言秽 -13.5278 ▁罪孽 -13.5279 ▁丁浩 -13.528 ▁瑞克 -13.5281 ▁FBI -13.5284 工夫 -13.5284 美奂 -13.5285 421 -13.5285 ▁限售 -13.5285 ▁DV -13.5286 讫 -13.5287 ▁速成 -13.5288 公公 -13.5289 ▁Paul -13.529 习气 -13.529 ower -13.529 579 -13.5292 ▁統一 -13.5292 体表 -13.5292 ▁浮料 -13.5295 妙妙 -13.5296 汉语 -13.5296 ▁崇文 -13.5299 ▁独木 -13.5299 ▁发际 -13.53 壒 -13.5302 ▁启闭 -13.5302 遗憾 -13.5302 ▁招工 -13.5302 ▁鬼谷 -13.5303 ▁下厨 -13.5304 ▁离散 -13.5305 ▁常备 -13.5305 动摇 -13.5306 151 -13.5306 相对 -13.5307 ▁bwin -13.5307 冶金 -13.5308 ▁荣登 -13.5308 芥 -13.5308 ▁广进 -13.5308 ▁貂蝉 -13.5308 ▁治水 -13.5311 ┻ -13.5311 ▁10.2 -13.5311 持續 -13.5311 ▁租售 -13.5312 攝 -13.5313 ▁贾诩 -13.5314 ▁家私 -13.5315 ▁送医 -13.5315 信赖 -13.5315 ▁正欲 -13.5317 ▁遇事 -13.5317 ▁一左一 -13.5318 ▁福寿 -13.5318 ▁光驱 -13.5319 ▁盐湖 -13.5319 彌 -13.5319 ▁柱塞 -13.532 ▁大惊小 -13.532 芳芳 -13.532 ▁足球赛 -13.5321 ▁柳絮 -13.5321 牽 -13.5322 609 -13.5323 ▁two -13.5323 ▁南无 -13.5325 ▁泄密 -13.5325 ▁桃红 -13.5326 乜 -13.5327 ▁干净利 -13.5328 ▁烟灰 -13.5329 ▁老丈 -13.5329 257 -13.5329 譙 -13.533 ▁推论 -13.533 欢庆 -13.5331 ▁鼻息 -13.5331 媠 -13.5332 ▁森林狼 -13.5333 race -13.5333 ▁昙花 -13.5334 ▁继母 -13.5335 一番 -13.5336 ▁体弱 -13.5336 痂 -13.5337 ▁邓超 -13.5337 ▁王珉 -13.5338 ▁力挺 -13.5338 ▁脑残 -13.5339 ▁專門 -13.534 ▁一邊 -13.5341 ▁規則 -13.5341 胫 -13.5342 ▁官能 -13.5344 ▁碳素 -13.5345 ▁丫丫 -13.5348 ▁数千万 -13.5348 骐 -13.5349 ▁每时每刻 -13.535 ▁所罗门 -13.535 宽恕 -13.5352 ▁九十九 -13.5355 ▁梳洗 -13.5355 ism -13.5355 ▁佛罗伦萨 -13.5355 ▁入夜 -13.5356 ▁轨制 -13.5356 ▁维基百 -13.5357 ▁黑黑 -13.5358 ▁西伯利亚 -13.5358 ▁Free -13.5358 ▁河马 -13.5359 撷 -13.5359 ▁张掖 -13.5361 east -13.5362 ▁亲卫 -13.5364 ates -13.5364 蟠 -13.5365 ▁认认 -13.5365 凑 -13.5365 ▁相對 -13.5365 ▁乙级 -13.5366 ▁装傻 -13.5366 岁月 -13.5366 緩 -13.5368 见识 -13.5369 ▁小猴 -13.537 2.9 -13.5371 珞 -13.5372 ▁分類 -13.5373 ▁安邦 -13.5374 ▁悻悻 -13.5376 參 -13.5377 ▁肺结核 -13.5377 ▁蒙古人 -13.5377 ▁兴安 -13.5377 ▁创维 -13.5378 ▁几滴 -13.538 519 -13.538 ▁差旅 -13.5381 ▁HDMI -13.5381 作画 -13.5381 ▁紧追 -13.5382 ▁HPV -13.5382 祢 -13.5384 ▁阿凡 -13.5384 ▁喜迎 -13.5384 ▁栀子 -13.5384 ▁韵律 -13.5385 512 -13.5385 ▁面不改 -13.5385 ▁森严 -13.5386 ▁烘托 -13.5386 侯爷 -13.5386 631 -13.5386 ▁查核 -13.5387 雏 -13.5387 ▁鸡尾酒 -13.5389 ▁滤料 -13.539 ▁串口 -13.5391 ▁雪上加 -13.5392 ▁艾玛 -13.5394 lc -13.5394 ▁逐级 -13.5395 屁股 -13.5397 816 -13.5397 宾馆 -13.5398 无惧 -13.5398 ▁肖华 -13.5399 ▁士林 -13.5399 ▁村支 -13.5399 788 -13.54 ▁周维清 -13.54 ▁Ke -13.54 ▁转嫁 -13.54 知识产权 -13.5401 暑假 -13.5404 ▁电荷 -13.5404 ▁艾斯 -13.5404 ▁永定 -13.5404 ▁暴虐 -13.5405 ▁血魔 -13.5407 ▁苏苏 -13.5409 ▁举目 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-13.9931 拏 -13.9933 氓 -13.9933 慘 -13.9938 鳃 -13.9938 菖 -13.9961 垩 -14 檯 -14 耦 -14.0032 膨 -14.0037 筜 -14.0038 砺 -14.0044 蟢 -14.0047 鸠 -14.0053 鍾 -14.006 锻 -14.0064 喅 -14.0064 ゥ -14.0066 苟 -14.0069 廈 -14.0073 氖 -14.0073 讹 -14.0091 摝 -14.0103 穢 -14.0103 厤 -14.0114 镕 -14.0114 怠 -14.0119 襪 -14.0129 寜 -14.0136 窇 -14.014 婀 -14.0142 剎 -14.0147 扒 -14.0157 噫 -14.0162 聽 -14.0168 懼 -14.0173 涣 -14.0175 啋 -14.0175 闱 -14.0184 蚠 -14.0188 稗 -14.0203 溏 -14.0205 儓 -14.0225 曘 -14.0225 飄 -14.0227 荧 -14.0231 犮 -14.0232 偗 -14.0243 苸 -14.0254 馗 -14.026 羔 -14.0284 琥 -14.0285 钂 -14.0287 懠 -14.0293 罵 -14.03 绛 -14.0307 啮 -14.0309 摱 -14.0318 幹 -14.0318 眳 -14.032 蓑 -14.0324 孳 -14.0335 γ -14.0342 蹊 -14.036 玳 -14.036 珪 -14.0364 颔 -14.0367 婼 -14.0369 睶 -14.0375 湳 -14.0378 崱 -14.0382 儆 -14.0391 檻 -14.0398 綱 -14.0398 檸 -14.0404 ┛ -14.0418 珅 -14.042 檄 -14.0425 鑿 -14.0431 楸 -14.0436 蚜 -14.0456 畲 -14.0458 睙 -14.0463 僭 -14.0465 ┒ -14.0476 湃 -14.0478 悥 -14.0487 ず -14.0492 嵎 -14.0492 崉 -14.0496 巩 -14.0498 碁 -14.0499 坥 -14.051 噹 -14.0515 簽 -14.0519 忖 -14.0524 蚥 -14.0539 羍 -14.0542 墽 -14.0544 膻 -14.0551 畀 -14.0553 癜 -14.0555 俠 -14.0562 渿 -14.0564 棯 -14.0571 傍 -14.0572 噶 -14.0579 铄 -14.0583 遼 -14.0589 憳 -14.0592 蕲 -14.0596 厠 -14.0601 虁 -14.0601 鸩 -14.0601 纨 -14.0603 韕 -14.0603 鸷 -14.0603 惕 -14.0605 嗪 -14.0605 銈 -14.0605 蝗 -14.0609 葡 -14.0611 髦 -14.063 姤 -14.064 鉛 -14.064 螭 -14.0649 岘 -14.0653 懶 -14.0656 頃 -14.0669 羧 -14.0674 燧 -14.0676 莪 -14.0695 暂 -14.0696 繇 -14.0697 跶 -14.0697 龜 -14.0699 褝 -14.0704 赅 -14.0706 嘢 -14.0708 擇 -14.071 о -14.0711 颢 -14.0736 郃 -14.0738 徹 -14.0754 臘 -14.0768 镭 -14.0771 Е -14.0771 誊 -14.0775 壆 -14.0778 酆 -14.078 犸 -14.0787 嚐 -14.0794 柩 -14.0803 砭 -14.0803 蝴 -14.0812 騼 -14.0812 慣 -14.0815 杵 -14.0815 灬 -14.0829 邨 -14.0829 颁 -14.0832 篪 -14.0833 偧 -14.0838 痚 -14.0843 執 -14.0849 祜 -14.085 蘊 -14.0854 屍 -14.0857 谚 -14.0861 诳 -14.0868 褚 -14.0882 ア -14.0885 綜 -14.0894 稞 -14.0897 蚑 -14.0901 洺 -14.0904 邙 -14.0908 厱 -14.0913 葫 -14.092 讧 -14.0925 伓 -14.0927 皎 -14.0927 既 -14.0941 頬 -14.0941 昉 -14.0948 碚 -14.0951 樊 -14.0954 櫎 -14.0955 苾 -14.096 窸 -14.0984 蜘 -14.0986 刽 -14.0991 剳 -14.0991 瓤 -14.0991 槛 -14.0991 庛 -14.0996 塩 -14.1005 蹦 -14.1008 寳 -14.1024 ▉ -14.1026 ト -14.1026 販 -14.1038 棂 -14.1043 斡 -14.1048 蚱 -14.1053 ┿ -14.1055 黜 -14.1062 ゼ -14.1067 惛 -14.1072 澍 -14.1079 骈 -14.1091 釘 -14.1098 薅 -14.1108 遜 -14.1108 亹 -14.111 儹 -14.1112 钏 -14.1115 驷 -14.1117 蛐 -14.1136 嫆 -14.1141 腭 -14.1144 諷 -14.1144 誼 -14.1158 紐 -14.116 奧 -14.1162 摔 -14.1163 聒 -14.1163 ˋ -14.1165 嬌 -14.1165 谡 -14.1168 蜇 -14.117 姙 -14.1172 鴨 -14.1175 ∧ -14.1189 鈴 -14.1199 妧 -14.1201 純 -14.1202 巖 -14.1209 骶 -14.1216 呋 -14.1221 彟 -14.1221 炙 -14.1224 枬 -14.1226 緬 -14.1226 恫 -14.1228 魍 -14.1228 倶 -14.1231 暎 -14.125 醺 -14.1252 仝 -14.1252 韹 -14.1262 ∕ -14.1272 囿 -14.1274 艷 -14.1279 冯 -14.1286 喹 -14.1294 晠 -14.1301 镔 -14.1321 р -14.133 琍 -14.133 渚 -14.1331 蓐 -14.134 ∏ -14.1343 谯 -14.1343 砙 -14.1345 湟 -14.1362 颛 -14.1365 唁 -14.1367 棦 -14.1372 舛 -14.1372 釀 -14.1377 哗 -14.1379 鸔 -14.1382 硩 -14.1385 舸 -14.1392 幺 -14.1396 诮 -14.1397 軴 -14.1397 侑 -14.1409 殣 -14.1409 輯 -14.1416 賊 -14.1429 潍 -14.1434 誦 -14.1436 靼 -14.1454 錧 -14.1456 賜 -14.1461 尯 -14.1481 痨 -14.1481 卮 -14.1484 リ -14.1494 呤 -14.1494 嚩 -14.1499 褟 -14.1501 璭 -14.1508 瘉 -14.1521 杲 -14.1523 墮 -14.1526 苣 -14.1533 鐍 -14.1533 辗 -14.1536 蚩 -14.1536 堅 -14.1537 贮 -14.1539 艌 -14.1546 芫 -14.1546 砒 -14.1553 窩 -14.1561 魃 -14.1566 憶 -14.1568 賠 -14.1569 濠 -14.1572 ゃ -14.1584 é -14.1586 懑 -14.1589 歙 -14.1589 愍 -14.1591 纥 -14.1599 熄 -14.1604 拺 -14.1614 喱 -14.1624 帿 -14.1624 忏 -14.1636 滂 -14.1642 簸 -14.1644 阇 -14.1647 ˇ -14.1657 卂 -14.1667 鸪 -14.1669 躺 -14.1673 ╔ -14.168 埚 -14.169 僮 -14.1695 耒 -14.1697 鍡 -14.17 浏 -14.1713 鉔 -14.1713 寵 -14.173 饴 -14.1743 藍 -14.1754 崕 -14.1758 徊 -14.1758 鲖 -14.1758 駁 -14.1764 旖 -14.1769 郅 -14.1776 囵 -14.1782 堀 -14.1782 毘 -14.1799 谘 -14.1802 霰 -14.1805 崏 -14.1815 縍 -14.1817 奄 -14.182 疥 -14.1823 垙 -14.1825 苡 -14.1828 疡 -14.1833 蚹 -14.1833 ̇ -14.1835 炁 -14.1835 锑 -14.1835 旸 -14.1838 晳 -14.1841 緷 -14.1843 脐 -14.1846 叴 -14.1848 婠 -14.1861 魉 -14.1874 灑 -14.1892 墉 -14.1895 縹 -14.1895 汜 -14.1903 俎 -14.1908 衅 -14.1912 彴 -14.1913 寔 -14.1916 甆 -14.1921 竊 -14.1924 簌 -14.1924 ∮ -14.1926 锗 -14.1929 溿 -14.1931 毊 -14.1939 エ -14.1947 妊 -14.1947 廁 -14.195 洹 -14.1952 葚 -14.1955 騷 -14.196 埮 -14.1963 萒 -14.1989 蘿 -14.1991 イ -14.1999 鵬 -14.2002 涮 -14.2002 ˉ -14.201 黏 -14.2011 ╰ -14.2015 迄 -14.2015 宑 -14.2023 粲 -14.2025 釣 -14.2025 鬩 -14.2031 亓 -14.2033 绚 -14.2044 姽 -14.2044 阖 -14.2045 咛 -14.2056 惫 -14.206 ̈ -14.2062 ㄢ -14.2062 砲 -14.2062 鞠 -14.2069 躲 -14.2071 匝 -14.2072 ハ -14.2081 叮 -14.2085 ニ -14.2086 禱 -14.2091 蛀 -14.2096 遑 -14.2097 鑜 -14.2102 崟 -14.2107 暝 -14.2107 撮 -14.2115 砥 -14.2118 蔺 -14.2123 镊 -14.2131 瞅 -14.2137 螟 -14.2142 呂 -14.2144 捺 -14.2153 埕 -14.2168 衙 -14.2174 咭 -14.2182 笆 -14.2182 胚 -14.2183 跎 -14.2187 纠 -14.2188 儳 -14.2195 爞 -14.2195 綑 -14.2206 媾 -14.2208 礽 -14.2208 暈 -14.2211 拭 -14.2212 胱 -14.2214 蹙 -14.2215 禩 -14.2246 禍 -14.2254 醐 -14.2254 ╜ -14.2257 鲢 -14.2262 傣 -14.2269 垵 -14.2273 萋 -14.2278 潸 -14.2281 韓 -14.2284 潑 -14.2286 ∈ -14.2292 嫍 -14.2292 ö -14.2294 僖 -14.2297 顛 -14.2302 勸 -14.2308 牡 -14.2312 嘈 -14.2313 趺 -14.2316 刎 -14.2319 鎳 -14.2321 韣 -14.2321 锰 -14.2322 榉 -14.2329 叻 -14.2338 甙 -14.2338 濃 -14.2347 熔 -14.2348 欤 -14.2351 葺 -14.2351 苻 -14.2359 睐 -14.2361 馳 -14.2365 溾 -14.237 锾 -14.2373 и -14.2384 佶 -14.2384 旳 -14.2386 悄 -14.2391 蒋 -14.2394 襕 -14.2395 每 -14.241 揆 -14.2414 帺 -14.2419 砆 -14.2419 汾 -14.2427 巹 -14.2427 蒼 -14.2427 赡 -14.243 旎 -14.2433 嘯 -14.2436 喋 -14.2438 裾 -14.2438 偛 -14.2446 蹟 -14.2446 陼 -14.2446 蠟 -14.246 揵 -14.2466 娩 -14.247 镐 -14.2472 蔼 -14.2477 搬 -14.2477 浐 -14.2479 乞 -14.2484 挹 -14.2485 頌 -14.2488 氅 -14.249 呵 -14.2497 饋 -14.2499 籠 -14.2504 椰 -14.2512 帚 -14.2515 馔 -14.2518 侈 -14.2521 嘻 -14.2527 犟 -14.2529 馒 -14.2543 漊 -14.2543 ╯ -14.2546 诌 -14.2548 嫑 -14.2551 贽 -14.2554 濫 -14.2562 豈 -14.2584 瓨 -14.2587 桠 -14.2601 殓 -14.2604 荪 -14.2604 疳 -14.2612 羯 -14.2613 攫 -14.2615 菸 -14.2618 釼 -14.2621 枒 -14.2635 潬 -14.2637 畼 -14.2651 碣 -14.2654 橏 -14.2657 逍 -14.2661 憬 -14.2662 彧 -14.2664 鐡 -14.2665 帹 -14.2674 氐 -14.2674 偵 -14.2682 雌 -14.2687 懲 -14.2688 睜 -14.2688 嗥 -14.2691 髰 -14.2691 糾 -14.2693 拈 -14.2694 吆 -14.2696 僑 -14.2696 卟 -14.2696 敮 -14.2699 梾 -14.2699 鄧 -14.2702 鬱 -14.2716 闩 -14.2719 亜 -14.2733 弰 -14.2736 炪 -14.2738 喌 -14.2744 掴 -14.2744 鈣 -14.2747 矜 -14.2753 谄 -14.2758 惞 -14.2764 蔛 -14.2767 叢 -14.2786 仞 -14.2788 姢 -14.2789 憉 -14.2792 ║ -14.2795 瘔 -14.2806 椈 -14.2812 繊 -14.2812 铖 -14.2815 涸 -14.2847 燚 -14.2855 苁 -14.2858 吭 -14.2858 泅 -14.286 た -14.2872 猗 -14.2875 裘 -14.2875 ∞ -14.288 圜 -14.2886 桵 -14.2892 嫃 -14.2895 孰 -14.2895 惁 -14.2903 掼 -14.2909 麂 -14.2912 燦 -14.2918 ∫ -14.2923 槁 -14.2932 铣 -14.2933 莖 -14.2935 ク -14.2955 蘇 -14.2962 喧 -14.2971 尋 -14.2977 睷 -14.2987 捱 -14.299 嗏 -14.2993 俚 -14.3001 肓 -14.3019 巻 -14.3022 蕤 -14.3022 し -14.3024 茴 -14.3029 岿 -14.3033 ∥ -14.3036 斂 -14.3039 蔣 -14.3042 鉆 -14.3048 觥 -14.3054 埡 -14.3056 轟 -14.3059 籲 -14.3062 尻 -14.3068 オ -14.3071 陜 -14.308 輻 -14.3083 篁 -14.3086 陰 -14.309 澘 -14.3091 軒 -14.31 惚 -14.3103 崲 -14.3103 瓕 -14.3115 阗 -14.3115 畹 -14.3121 哦 -14.3123 貉 -14.3124 岬 -14.3132 嬰 -14.3135 圻 -14.3135 踒 -14.3138 縺 -14.3141 嫭 -14.3147 佥 -14.3159 砣 -14.3162 撐 -14.3167 溉 -14.3173 ッ -14.3182 崃 -14.3191 薨 -14.3197 臬 -14.3209 鸯 -14.3215 绉 -14.3227 蘍 -14.3233 カ -14.3236 篙 -14.3247 芾 -14.325 値 -14.3253 咤 -14.3255 笄 -14.3271 闽 -14.3279 瑰 -14.3282 崌 -14.3283 菟 -14.3286 媯 -14.3292 虢 -14.3292 嗜 -14.3297 噦 -14.3307 斫 -14.3325 畢 -14.3328 狒 -14.3328 絨 -14.3328 诡 -14.3333 堃 -14.3349 鹹 -14.3349 丼 -14.3355 铬 -14.3358 珥 -14.3376 繋 -14.3376 緥 -14.3379 熵 -14.3385 淸 -14.3388 隸 -14.3391 緝 -14.3394 諎 -14.3397 桡 -14.3403 鍨 -14.3409 ぜ -14.3412 玠 -14.3418 祿 -14.3421 ǒ -14.343 滯 -14.3433 膑 -14.3433 崧 -14.3445 茓 -14.3445 澗 -14.3454 ナ -14.346 徆 -14.3463 咋 -14.3475 蜻 -14.3479 儫 -14.3482 伢 -14.3485 賬 -14.3488 粸 -14.3488 珒 -14.35 蔥 -14.3503 ロ -14.3506 纏 -14.3506 蚵 -14.3506 枂 -14.3521 説 -14.3524 啉 -14.3527 坷 -14.353 蚤 -14.3536 ❀ -14.3552 廂 -14.3552 赟 -14.3552 鰯 -14.3555 瞾 -14.3558 弁 -14.3561 邰 -14.3561 嶼 -14.3579 芨 -14.3579 殁 -14.3586 戊 -14.359 亶 -14.3592 彥 -14.3604 蒡 -14.3604 雎 -14.3619 驚 -14.3622 鲍 -14.3622 誯 -14.3622 眙 -14.3629 叱 -14.3634 齆 -14.3644 茏 -14.3647 獾 -14.3653 サ -14.3656 掬 -14.3671 鷹 -14.3672 珷 -14.3675 喑 -14.3678 餓 -14.3678 棝 -14.3681 韋 -14.3681 湧 -14.369 兖 -14.3696 蜗 -14.3709 兌 -14.3709 秣 -14.3709 馮 -14.3718 簻 -14.3734 繍 -14.3734 紩 -14.3746 倆 -14.3753 荑 -14.3753 凄 -14.377 猥 -14.3771 皺 -14.3771 盃 -14.3771 寑 -14.3781 淦 -14.3781 舂 -14.3781 櫓 -14.3784 ˊ -14.379 ゚ -14.38 枞 -14.3806 魈 -14.3806 粛 -14.3822 茭 -14.3828 佗 -14.383 遐 -14.3834 暱 -14.3837 鍥 -14.3837 棗 -14.384 刕 -14.3844 鍿 -14.3844 孀 -14.385 凇 -14.3869 塬 -14.3869 镍 -14.3873 箟 -14.3875 瓮 -14.3882 跖 -14.3885 鎿 -14.3888 鄏 -14.39 憤 -14.3907 叿 -14.3913 酊 -14.392 蹴 -14.3929 レ -14.3932 臑 -14.3939 鵝 -14.3939 寷 -14.3951 彞 -14.3951 ╟ -14.3955 湊 -14.3955 崛 -14.3963 窠 -14.3964 笟 -14.3967 櫥 -14.397 炕 -14.3976 蘆 -14.3983 鋅 -14.3983 箰 -14.3999 蹭 -14.4002  -14.4002 礊 -14.4006 萻 -14.4031 衄 -14.4038 猊 -14.4044 毚 -14.4054 瑀 -14.4054 碜 -14.4063 蠹 -14.4063 銜 -14.4067 鬡 -14.4067 剝 -14.4076 绺 -14.4076 ˹ -14.4083 缢 -14.4083 緒 -14.4088 + -14.4106 愘 -14.4109 鶴 -14.4111 叺 -14.4115 霹 -14.4115 這 -14.4117 仺 -14.4118 镛 -14.4122 烩 -14.4127 迂 -14.4128 旃 -14.4131 蓼 -14.4131 诅 -14.4141 墐 -14.4144 鳶 -14.4151 ┐ -14.416 痼 -14.416 懗 -14.4164 棷 -14.4167 缰 -14.4173 笳 -14.4183 箜 -14.4183 吒 -14.4191 誇 -14.42 龔 -14.4206 粐 -14.4213 浒 -14.4218 彭 -14.4223 谔 -14.4226 鲫 -14.4229 懇 -14.4229 癒 -14.4229 啾 -14.423 箸 -14.4232 雓 -14.4236 鮛 -14.4242 泯 -14.4253 ě -14.4258 殦 -14.4262 綈 -14.4262 钿 -14.4272 鲷 -14.4275 甾 -14.4288 臢 -14.4295 檮 -14.4314 赁 -14.4331 嚯 -14.4331 掋 -14.4334 フ -14.4341 琵 -14.4341 笇 -14.4344 缮 -14.4347 蝈 -14.4354 匮 -14.4354 禺 -14.4357 畿 -14.436 囔 -14.4372 垃 -14.4374 悌 -14.4374 焖 -14.4374 な -14.4377 悱 -14.4377 乭 -14.4381 犍 -14.4381 ╮ -14.4387 諦 -14.4387 讥 -14.4408 唛 -14.4414 祕 -14.4414 挣 -14.4415 貞 -14.4421 媱 -14.4427 н -14.4437 紆 -14.4451 逞 -14.4453 耋 -14.4454 菓 -14.4457 £ -14.4464 悼 -14.4472 締 -14.4484 俅 -14.4491 蹑 -14.4494 肛 -14.4497 痪 -14.4504 ァ -14.4504 噼 -14.4511 嵬 -14.4511 啒 -14.4525 闖 -14.4525 遒 -14.4528 陟 -14.4531 儨 -14.4538 粎 -14.4538 ケ -14.4548 峒 -14.4548 テ -14.4552 泱 -14.4558 忒 -14.4569 崭 -14.4596 暹 -14.463 橄 -14.4637 搐 -14.467 屉 -14.4673 邓 -14.4694 阮 -14.4726 採 -14.4738 蜓 -14.476 谩 -14.4816 窿 -14.4924 豺 -14.4945 殡 -14.4946 婪 -14.4983 仍 -14.5014 玻 -14.5046 鱿 -14.5169 熨 -14.5196 掃 -14.5222 遵 -14.5236 郜 -14.5281 嫖 -14.5292 肪 -14.5303 劾 -14.5324 螨 -14.5366 拯 -14.5415 預 -14.5438 鳄 -14.5502 惰 -14.5547 廢 -14.5589 踐 -14.5599 瘠 -14.5625 衮 -14.5635 堕 -14.5635 钨 -14.5658 紹 -14.5666 芎 -14.567 铂 -14.5674 龈 -14.5678 茁 -14.5712 矸 -14.5777 蠕 -14.5786 荀 -14.588 纜 -14.588 忤 -14.5892 唬 -14.6026 浜 -14.6047 噩 -14.606 黔 -14.6066 嫦 -14.6123 榈 -14.6131 咖 -14.6139 髅 -14.6151 镶 -14.6212 奂 -14.6233 戬 -14.6237 莅 -14.627 毋 -14.6286 圾 -14.6286 敝 -14.6302 罂 -14.6319 徇 -14.6367 沒 -14.6384 纫 -14.6428 柑 -14.6434 咄 -14.6461 骼 -14.6489 楹 -14.6514 嚅 -14.6522 佼 -14.6535 刪 -14.6539 甬 -14.6543 荤 -14.6573 藓 -14.658 甥 -14.659 聂 -14.661 狈 -14.6613 郸 -14.6718 阎 -14.6777 寬 -14.6803 珮 -14.6816 斟 -14.6816 饲 -14.6839 瘪 -14.6844 掰 -14.6846 惋 -14.69 锺 -14.6946 蟆 -14.6947 椹 -14.6951 骜 -14.6964 秸 -14.7003 貢 -14.706 惦 -14.7099 菏 -14.7169 稔 -14.7173 蛔 -14.72 榛 -14.7262 颓 -14.7267 扪 -14.7279 沭 -14.7288 腋 -14.7357 橇 -14.7395 噴 -14.7398 閭 -14.7403 俞 -14.7463 咧 -14.7466 棣 -14.7559 鹦 -14.7568 妪 -14.7577 蹿 -14.7658 懵 -14.7663 嬴 -14.7682 赂 -14.7724 哺 -14.7742 枸 -14.7743 拷 -14.7743 訾 -14.7818 嗫 -14.7869 涓 -14.789 薦 -14.7933 覃 -14.7935 憧 -14.7973 詹 -14.7978 儡 -14.8035 娄 -14.8189 稣 -14.8312 烘 -14.8314 刨 -14.8318 嘀 -14.8319 蜈 -14.8377 炅 -14.841 橡 -14.8418 蜕 -14.8445 鹑 -14.8466 谆 -14.8511 诧 -14.8526 诽 -14.8541 纾 -14.8677 另 -14.8698 漉 -14.8709 卤 -14.876 餮 -14.8766 狰 -14.8833 亳 -14.891 阱 -14.8932 阆 -14.8958 殘 -14.901 甄 -14.9069 硼 -14.9085 鍍 -14.9091 毂 -14.9165 潛 -14.9195 挎 -14.9277 叵 -14.9305 耽 -14.9405 荥 -14.9481 鑰 -14.9529 汲 -14.9586 螳 -14.9649 阐 -14.9729 濡 -14.9776 鸵 -14.9796 奚 -14.981 奘 -14.9819 牺 -14.9882 脅 -14.9899 谲 -14.9928 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▁работ 292621 ▁Jehofa 292617 indu 292598 ▁גי 292597 ларды 292575 系统 292549 alde 292545 ▁ohne 292539 ▁Προ 292534 季 292493 лиза 292474 ပဲ 292468 ρυ 292467 ▁රට 292466 ▁बार 292459 ラン 292458 ▁toute 292444 gest 292441 ▁arra 292432 ▁დას 292414 ▁Comments 292413 っても 292371 obh 292350 되는 292314 ▁thanh 292298 мой 292251 ಫ 292230 ▁Gem 292218 ▁жан 292207 ▁እያ 292183 ▁કોઈ 292149 ▁kuring 292131 äis 292130 ilio 292115 ▁amer 292105 ▁वाले 292063 ▁човек 292049 ▁Δεν 292037 ▁thay 292026 ▁практи 292012 ▁לפני 291989 тің 291982 ναν 291981 ▁ceva 291977 ▁hyvä 291968 ▁makko 291968 пита 291949 ՞ 291945 ▁sering 291939 այն 291931 เข 291917 ▁tega 291915 ▁დი 291876 Ag 291870 ▁Put 291863 ▁anders 291858 ▁skip 291848 чку 291815 ▁Feder 291804 endes 291797 ▁utama 291790 ▁отде 291780 ▁která 291776 abang 291753 ▁Rap 291746 bak 291732 ▁ûnder 291715 තුව 291668 ▁Ελλάδα 291668 ▁Ге 291664 ▁таких 291650 udar 291631 ▁ນີ້ 291621 ką 291617 ▁buah 291613 ần 291590 ▁ថ 291575 ▁សម 291549 arl 291536 批 291535 dés 291529 にな 291521 ▁دارند 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▁Paulo 289358 ezet 289355 因此 289348 льная 289342 דן 289254 ▁ሁሉ 289224 YO 289211 ▁alat 289197 alay 289180 gro 289166 ▁biến 289160 ▁ers 289159 рында 289151 ▁Lah 289133 /19 289122 icy 289118 ▁помо 289115 elő 289114 өк 289103 ▁ਨਾ 289098 ▁associ 289091 残 289081 ▁часто 289074 ión 289073 ▁моло 289065 ▁фе 289064 τον 289040 ▁गर्दै 289037 ತ್ತು 289035 ▁direk 289030 ▁säger 288995 টা 288994 eur 288989 ▁услови 288987 ieni 288979 ▁saman 288978 ▁hori 288965 ყვ 288925 ▁drag 288921 ▁Už 288920 ພາບ 288914 וץ 288908 віч 288901 ▁सबै 288859 ▁terra 288834 ▁nelle 288802 וך 288796 ▁departament 288791 ▁Mid 288787 As 288783 活動 288735 ▁ενώ 288727 환 288723 ịa 288710 ▁Maz 288650 inat 288646 туры 288639 خص 288635 лина 288630 ത്തു 288621 ▁Европа 288619 ▁ukuk 288605 ▁origin 288596 ▁student 288595 ▁ನಲ್ಲಿ 288592 餐 288582 ▁memberi 288572 aktiv 288567 疑 288566 ▁Raz 288556 اته 288547 ▁Christ 288531 ▁सब 288522 נען 288492 لې 288477 ▁ember 288454 ▁tilf 288413 раҡ 288403 сні 288395 ▁lep 288393 ▁Гру 288393 ▁nako 288378 职 288355 ▁atunci 288347 ▁sale 288347 ▁ठ 288290 mine 288271 ▁ಬಗ್ಗೆ 288256 odh 288255 ▁kari 288233 ▁casos 288201 тыр 288199 cionar 288165 ▁մեկ 288144 %) 288140 તે 288132 дува 288105 яр 288105 ▁للت 288104 mk 288091 ▁añu 288088 ▁iray 288083 ણે 288076 ▁đạo 288060 ▁eens 288051 ▁외 288033 мон 288017 ▁nuestra 288017 روں 287991 нера 287985 ែន 287966 olan 287959 けて 287935 某 287932 ując 287916 ▁उनले 287910 들을 287891 гар 287836 ▁Мен 287833 cam 287821 能够 287808 ▁Ihr 287797 ▁Một 287796 ▁hög 287790 ವಾದ 287757 irdi 287740 ▁home 287736 гло 287732 ▁wada 287731 ▁ម៉ 287724 ▁bästa 287701 ezen 287700 ▁ביותר 287700 ▁zgod 287660 ▁rätt 287647 ရဲ႕ 287628 ▁ვა 287626 ▁Kv 287615 ▁Tuy 287600 okka 287598 ▁اعلان 287596 adaha 287586 redi 287583 aber 287580 ▁giáo 287560 ائية 287532 িয় 287522 ▁kész 287491 irim 287490 кие 287488 ▁lập 287476 ème 287470 elele 287459 ▁හරි 287454 ▁önce 287436 ਵਾਂ 287420 ▁ಕೆ 287419 ▁නැති 287397 รัก 287368 ▁tall 287359 čila 287343 ática 287340 ▁هیچ 287297 ▁iOS 287272 ▁lille 287257 શન 287227 議 287212 ▁дости 287201 ▁கி 287162 ▁집 287160 ▁sowie 287159 ɔ 287148 ▁טוב 287123 ▁Köz 287115 qal 287102 ▁nuestro 287098 ▁편 287079 сня 287068 ▁MAR 287059 ையை 287052 ▁medlem 287048 ▁يح 287045 rend 287039 gio 287036 ▁islam 287033 ▁હતો 287032 ▁лучше 287030 務 287021 ▁ට 287018 ▁Green 287017 adir 286969 ств 286969 ▁উ 286939 ▁bà 286931 દા 286883 ಂದು 286880 ျခင္း 286877 γγ 286854 זר 286840 ▁سوال 286836 osten 286798 ▁según 286759 ▁dois 286753 ▁BER 286745 ▁toga 286741 вят 286727 тек 286714 ▁лока 286699 ”- 286654 qala 286645 ▁Tech 286632 ▁коле 286630 වර 286593 ▁qaba 286577 hub 286557 MI 286556 ضاء 286543 ▁vän 286535 ▁seura 286529 ▁punk 286524 ▁supaya 286511 ▁bylo 286458 กรรม 286443 зор 286390 oute 286377 ยา 286375 திய 286357 ದಿ 286349 ▁więc 286340 ▁тэр 286310 даў 286308 gad 286299 ▁tiên 286283 Ụ 286281 ▁bho 286269 ▁ard 286239 ეტ 286236 ften 286232 ▁ešte 286209 ▁લે 286207 ▁69 286184 ▁Concello 286183 ▁стан 286181 ▁ł 286173 ▁izao 286168 asje 286161 ▁Фран 286126 није 286093 ▁iwwert 286086 ▁تھے 286069 ▁가장 286043 ▁група 286035 mais 285981 äich 285964 ▁hali 285959 ljiv 285954 minen 285933 ▁jossa 285921 ▁20- 285908 amiseks 285885 ▁Stra 285881 ▁Ло 285881 ▁Dövlət 285859 дава 285849 andro 285840 ▁ثم 285832 ▁1996 285822 щите 285819 nye 285793 ▁अधिक 285776 ▁කෙ 285766 ▁lòt 285754 лыг 285728 益 285686 拍 285671 ▁тек 285659 ्स 285658 čnih 285646 ▁פא 285636 രെ 285621 ▁diễn 285620 დგ 285619 estas 285561 ▁gob 285523 ▁rodz 285506 ▁daugiau 285463 ▁fase 285463 क्ट 285455 ющий 285448 okk 285441 ନ୍ତୁ 285422 наль 285399 ▁ខែ 285372 ▁educ 285361 ▁אויס 285344 tev 285337 ▁gdy 285310 ▁alın 285289 ▁ресур 285278 ▁୧ 285237 læg 285223 зар 285223 ുകള 285215 ಚ್ಚ 285210 ▁yera 285207 HI 285197 ▁taba 285191 ▁yonke 285183 ▁dễ 285170 zne 285165 ▁categoria 285148 ▁ieg 285132 ▁rett 285128 ▁شوی 285107 54 285072 ▁қала 285067 itto 285064 ндә 285055 ▁dó 285015 ▁тр 284991 ibile 284975 ấn 284974 ited 284971 skar 284970 ਸਾ 284964 ▁job 284955 зво 284953 ▁сво 284893 ▁шарттар 284863 ▁ליי 284861 ନ୍ଧ 284856 ▁گه 284852 ଘ 284843 ▁быў 284827 ▁물 284795 ನಾ 284787 ▁напо 284787 ያን 284778 ğu 284769 ▁Evrop 284767 ▁орта 284762 ▁particular 284708 ▁1995 284697 ▁सकते 284675 лардың 284658 гда 284623 ▁Spe 284610 ▁Teh 284603 ▁вэ 284598 لق 284595 めて 284578 бора 284513 arik 284510 ▁Ori 284510 ▁Ανα 284505 lle 284469 ▁22, 284467 ለን 284437 ület 284432 ▁össze 284375 ▁Ken 284353 erb 284321 ▁Nev 284314 导 284307 ▁izm 284300 шення 284278 ▁يكون 284268 ▁èra 284266 лал 284264 ▁Bot 284238 কার 284225 ▁düşün 284224 ▁sense 284210 ਕੇ 284204 ▁والا 284201 ▁yihiin 284184 ▁આવ 284173 ▁१० 284139 ข้อ 284123 sies 284122 ▁kirjo 284117 യോ 284114 ▁الي 284114 ▁plats 284056 ស្រ 284041 ▁Dans 284023 2] 284022 ija 284002 نج 283989 ▁våra 283982 ▁Lig 283978 ▁Ost 283967 ΙΑ 283945 ▁қазақ 283929 ूल 283901 even 283870 ▁postup 283867 ▁بش 283845 ▁hie 283840 ▁duten 283825 ▁Pay 283824 ▁ഞ 283795 ▁seluruh 283792 ină 283781 rus 283779 იკ 283777 ▁Kinder 283772 дж 283757 onin 283728 ▁fiind 283721 ▁رئيس 283714 ▁μέσα 283710 תה 283689 ▁byla 283678 ▁أس 283678 ▁Tem 283677 方式 283660 ல்ல 283635 ረው 283635 ய் 283625 ▁موا 283617 ▁رسول 283597 ברי 283560 डि 283555 ▁kwez 283550 ▁план 283545 ષ 283544 ▁എന്ന് 283538 ▁բոլոր 283527 认为 283512 ▁ره 283499 ړې 283497 وط 283487 ▁عبدال 283471 ▁програм 283467 ണ്ടി 283452 に入 283450 ▁சா 283444 ሴ 283419 кай 283408 બા 283396 ▁estão 283361 각 283349 ▁Life 283346 dagi 283345 міз 283333 նդ 283328 ▁फिर 283322 cje 283276 ▁цр 283270 awa 283259 ლობა 283255 ▁vực 283238 ▁जिल्ला 283224 ▁աշխատ 283222 કાર 283219 ▁Thông 283218 ▁varit 283218 ▁huu 283205 ▁आता 283187 ▁વર્ષ 283175 ବି 283146 akon 283134 ஸ 283129 ඩ් 283127 ▁гре 283101 ▁sätt 283092 ▁COM 283089 āli 283082 ▁март 283076 ▁kyllä 283069 ▁정보 283038 ▁zelo 283021 рг 283017 直接 283017 ոլ 283013 ▁مقابل 283000 ▁use 282987 ▁keessa 282975 ▁автор 282971 ▁בו 282936 ▁nõ 282931 ▁роботи 282921 ▁тільки 282914 ሶ 282910 ▁1/ 282910 леу 282908 თხ 282884 чено 282852 试 282830 ▁ہر 282811 ukela 282810 ניה 282805 ▁grunn 282786 эй 282773 判 282767 ▁वह 282756 ▁کرنا 282754 ▁rekl 282748 ▁தே 282744 ▁Dob 282740 ▁juba 282737 ▁ولكن 282729 張 282717 ▁україн 282708 ក្នុង 282692 との 282680 āla 282666 样 282650 richt 282618 ▁reform 282604 ौं 282593 стр 282563 ველი 282534 ▁किसी 282502 ▁december 282493 ▁macam 282479 ▁алу 282467 ▁Ռ 282466 ▁chơi 282462 ▁занима 282446 heet 282443 araa 282430 զ 282417 这是 282402 ▁Hans 282391 ▁minha 282339 ίων 282319 ▁eman 282312 問 282312 ▁հար 282304 තාව 282297 生产 282253 non 282240 ාවේ 282232 ▁monitor 282202 ▁cic 282189 porte 282173 umia 282141 ▁Neg 282124 ▁очи 282119 守 282117 ▁ہوں 282098 ніка 282096 čně 282078 ▁Bern 282070 ्यू 282068 ▁arch 282053 ்ப 282033 ▁וכ 282023 ▁власти 282018 etak 282011 ▁open 282009 بط 281995 发现 281973 stv 281945 ▁4: 281944 ▁Eks 281940 ▁toutes 281925 ▁виде 281921 ▁chia 281912 ▁heim 281893 ▁დამ 281871 ilishi 281864 ▁ብቻ 281863 ▁mám 281830 ▁ඔහු 281826 ật 281823 ▁ɗum 281793 ▁tarix 281747 ▁diren 281730 ▁llu 281700 ▁kaos 281698 amad 281688 നും 281682 لت 281666 ▁recursos 281655 ▁Фе 281641 кім 281610 ogra 281606 ▁תק 281603 ▁igual 281572 ▁file 281570 情况 281562 ked 281529 edet 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▁sikker 256289 ▁അത് 256287 inet 256286 ▁සා 256270 ▁kerajaan 256266 ường 256265 हर 256259 ▁fundit 256259 ▁تھی 256258 ▁escrit 256253 ൃ 256246 ▁тус 256235 ▁Llicencia 256229 гээр 256201 Tr 256194 ▁வேண்டும் 256181 λλη 256169 ▁йәш 256162 ▁ră 256161 alna 256157 ▁էին 256148 ▁тамам 256140 ▁kartu 256135 夏 256127 odzi 256121 पाल 256115 ▁мину 256115 ▁altijd 256087 ▁मौ 256072 ▁дуже 256061 ಡೆ 256045 րան 256035 ........ 256018 мини 256018 ▁1992 255978 ▁vaikka 255973 onc 255969 ▁Ax 255961 sor 255960 ▁நான் 255959 ▁oare 255956 ▁Tir 255950 关系 255948 曾 255947 ▁pate 255944 근 255917 ▁דורך 255897 λω 255896 ▁prieš 255888 ▁həm 255883 ผิว 255882 ▁θέ 255868 ając 255863 ▁marang 255860 ▁Ś 255851 ▁alo 255836 ▁стран 255835 ▁jelent 255808 ▁dabei 255804 ▁гро 255799 ▁Dunia 255796 ▁시작 255791 ▁목 255778 ▁positiv 255764 ולים 255762 ász 255752 së 255750 ▁Pes 255715 ingin 255706 ▁Eta 255706 ▁curso 255699 ▁hinn 255685 нік 255678 ▁قرآن 255675 ▁30, 255666 ập 255650 ▁beli 255645 ▁leurs 255629 ന്നി 255621 ▁niż 255614 ľa 255592 ASI 255591 cionais 255588 ▁cob 255583 tein 255568 ▁mess 255541 шча 255532 ▁möglich 255532 hlas 255527 ▁Zäit 255518 ложе 255491 ned 255459 ▁recept 255434 Nkulunkulu 255411 בט 255408 ▁ایجاد 255395 inkin 255393 ▁اون 255380 ▁разрабо 255343 pren 255339 ▁CS 255337 ▁paggamot 255336 ცია 255322 ගන්න 255311 ▁ahli 255307 ▁ļoti 255302 ▁मिल 255297 ▁نوم 255289 ivos 255281 ▁സമ 255278 ▁bada 255245 ▁està 255243 ▁ms 255228 ▁tradicional 255204 lut 255189 itsi 255177 सन 255174 ▁astfel 255135 რულ 255104 ▁enige 255096 τρ 255095 aco 255082 ▁bwe 255074 ▁შენ 255069 ੍ਰ 255064 isit 255057 人员 255057 ▁wab 255056 दि 255045 今天 255042 ્યું 255039 otti 255037 нак 255028 ▁Stand 255015 ▁Hir 255014 pert 254995 危 254977 取り 254973 чина 254967 ▁عدالت 254967 ped 254965 lju 254948 ▁життя 254939 irka 254933 ▁Fors 254931 යින් 254881 第二 254878 ▁હતું 254875 amet 254853 wart 254830 ▁thư 254829 ▁bulate 254799 ▁города 254797 ▁collec 254779 يار 254775 ▁ລະ 254754 ilh 254730 ▁larg 254710 utte 254696 hum 254684 ▁ҡарағыҙ 254665 गत 254644 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239545 ▁በዚህ 239542 ▁کړ 239491 եռ 239485 울 239467 ън 239455 ▁temperatur 239435 њето 239430 etten 239427 ▁לאחר 239427 ▁OG 239420 ร้าน 239418 зем 239398 典 239381 喜 239379 ▁GPS 239368 ▁கொண்ட 239363 ijen 239347 üg 239336 Ğ 239336 ▁Norsk 239323 ▁זייער 239302 ▁doon 239299 ников 239298 ▁maanta 239297 ▁പറഞ്ഞ 239294 ekte 239284 דיק 239269 onie 239266 ▁keli 239264 ▁vsak 239261 ▁stora 239258 إ 239238 ▁Mei 239224 ▁cai 239210 cian 239193 ▁איבער 239180 ▁stellen 239178 ▁(+ 239165 រដ្ឋ 239133 ▁አባ 239128 recht 239119 ▁합니다 239117 ▁need 239113 ▁мно 239104 ▁үр 239103 യു 239072 ▁केली 239068 наў 239060 ต้องการ 239046 ▁talo 239035 ناء 239029 pọ 239026 ▁образова 239024 υν 239017 wing 239000 ▁свое 238994 ▁добра 238980 ▁Internacional 238978 inea 238975 ogr 238968 masını 238951 ▁IR 238950 ▁gand 238950 ▁dhex 238949 ▁daerah 238940 angka 238934 ຟ 238928 ▁passat 238912 ிகள் 238900 ▁ahaan 238898 奥 238874 یره 238873 ▁lög 238873 不断 238832 とい 238831 ▁informatie 238809 ở 238788 ▁بلا 238771 800 238757 結果 238750 ▁miesz 238749 ▁probl 238744 ▁טר 238732 ями 238726 ▁birlikte 238726 طا 238713 领 238679 ▁انہوں 238678 ▁prol 238673 льни 238672 ୭ 238661 ભા 238648 drag 238638 ▁kasih 238631 mě 238615 ▁profess 238614 ▁지난 238607 택 238607 žd 238583 ▁Smart 238580 ▁usu 238561 कै 238560 ▁ersten 238558 ▁2007. 238539 ▁Rez 238535 ▁реше 238530 ▁Kw 238522 ▁געשריבן 238515 ▁câu 238514 ▁dä 238514 ▁ennen 238504 ▁Î 238464 ▁Jon 238459 参 238451 ライ 238446 ▁norma 238433 ▁meine 238429 iton 238428 ▁proceso 238427 ▁рублей 238426 ▁člen 238398 icher 238397 ▁Sn 238381 देखि 238375 ▁Cla 238371 ▁déjà 238358 ೂರು 238352 ▁usar 238349 ▁Bha 238317 ombe 238315 ▁Wall 238307 ▁ከመ 238304 ▁стране 238270 ▁වැඩි 238268 ալի 238254 larından 238242 дор 238237 ▁сая 238237 ▁voll 238236 ▁heller 238205 isser 238156 ▁Wel 238155 ײ 238152 джа 238143 ▁kerak 238132 ნის 238115 asia 238113 ▁İl 238112 发生 238102 rë 238090 ▁Isa 238086 ▁Lä 238067 ▁mientres 238042 uja 238038 ▁konfer 238038 ▁ניט 238033 ▁९ 238017 ▁Башҡортостандың 238016 洗 238016 ▁ଯେ 238008 ▁فرا 238000 と思う 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▁зро 202020 ▁Ton 202018 кола 202011 ▁тай 202006 ▁በኋላ 202006 ▁afect 201993 χρο 201991 уі 201975 ▁жі 201974 ▁مصرف 201971 転 201957 ▁rapport 201938 독 201934 ▁ọzọ 201933 ▁precum 201932 哪 201926 ▁kompleks 201917 ຸມ 201915 прави 201903 ▁mellett 201900 ▁0.0 201897 เคย 201894 ▁केला 201887 ▁bisnis 201886 县 201878 ▁дре 201865 КИ 201852 elia 201844 ▁EST 201841 ▁tashkil 201838 ▁Chủ 201834 ▁bomo 201829 ▁pogled 201825 ▁घेत 201815 追 201814 ▁ኦ 201806 ▁teks 201805 ▁இருந்த 201803 ▁आर 201795 ▁ок 201788 ▁vise 201774 elling 201765 ▁ovog 201764 ▁jeśli 201759 ▁Mini 201749 ▁stof 201742 ▁fuer 201740 ▁tycker 201737 ೊಂದು 201736 too 201730 ▁можуть 201727 ▁inizi 201704 ▁alcan 201699 ultur 201681 ўля 201670 ▁участие 201668 ანის 201660 ▁note 201652 ▁아니 201650 вить 201646 וון 201646 ▁яких 201635 ▁yat 201634 ▁zwar 201620 ▁dëse 201619 ▁trgov 201617 沙 201597 στ 201591 ୀଙ୍କ 201590 ▁napr 201589 ▁Nyt 201577 ▁hapa 201576 ▁asal 201570 ▁rekom 201566 โม 201565 ▁personn 201553 ▁Cin 201543 ▁hemen 201541 zij 201538 介 201524 ERA 201519 ન્સ 201515 ▁Tid 201510 ▁nauj 201490 ▁cell 201471 ▁potem 201460 live 201447 ▁નો 201445 ▁Ora 201440 angs 201432 нути 201430 ▁kib 201428 ▁fred 201422 ▁قائم 201406 ommen 201402 ській 201401 ぐ 201400 ▁stadig 201398 ▁финансо 201398 ▁спре 201389 ▁живе 201385 aff 201377 ▁պայման 201367 ▁તા 201360 ▁வழ 201354 ▁vode 201349 קום 201348 ▁භ 201344 तून 201343 ▁расте 201337 ▁fay 201335 igra 201321 کہ 201321 ▁hartu 201316 ▁gång 201312 ▁каче 201311 ▁hoog 201298 ▁Ұ 201297 ▁stup 201294 ▁biztos 201293 ással 201291 ▁хәбәр 201284 ▁uvijek 201282 hun 201271 ▁Fond 201266 ▁prea 201257 ▁Priv 201249 :11 201234 ▁ମେ 201234 森 201234 ljena 201214 ▁them 201209 όμε 201197 ▁cump 201174 ▁агент 201162 ▁tatt 201155 ियन 201147 ệp 201141 링 201116 ▁аң 201111 ▁повы 201110 ขอ 201094 必须 201088 ▁द्वारा 201087 тали 201078 ▁Ende 201070 ▁فل 201070 सम 201065 ▁chương 201062 ▁každ 201060 edik 201058 ▁difícil 201053 جع 201052 ଢ 201039 ▁କିଛି 201021 ▁MT 201011 ▁94 201003 ▁ME 200978 ▁وار 200963 чид 200961 ▁Bila 200947 交流 200947 ▁көр 200946 ▁זאל 200916 ▁piel 200913 ▁blevet 200908 ▁którym 200908 ▁часть 200907 ្ត 200896 bie 200886 രിച്ചു 200881 άζ 200878 анд 200867 ▁rhan 200864 ånd 200862 ▁maro 200853 ਸਤ 200850 คง 200846 ▁Salaam 200842 센터 200836 きた 200830 arp 200827 รวม 200815 ▁inim 200815 อุ 200812 ▁ezin 200809 لمان 200808 ivas 200807 യ്ക്ക് 200799 ಲಾ 200793 jm 200792 ▁bomb 200782 zić 200781 æs 200779 ▁választ 200753 ▁dəfə 200740 ▁тәр 200738 ▁rétt 200737 -25 200724 ცა 200705 ៊ី 200701 ▁quant 200682 ljeno 200674 ማት 200674 сө 200671 ▁स्थानीय 200658 क्र 200653 anaan 200651 bara 200651 ▁случай 200649 sus 200637 ▁intern 200636 ▁عالم 200630 ପା 200626 ▁Pris 200626 benn 200623 lla 200621 őr 200619 ▁ξ 200617 ียว 200606 计划 200597 ▁Afaan 200590 ▁RA 200574 сили 200572 ▁carta 200563 .— 200539 issement 200539 ▁делать 200529 ▁zamanda 200523 ▁ඩ 200520 sla 200499 ბი 200499 ▁Geld 200496 ▁fif 200493 ▁must 200490 ▁secondo 200474 จุด 200463 olf 200462 是在 200456 ▁שפּיל 200443 ▁міні 200441 ▁entra 200439 ▁կան 200439 ュ 200421 ▁dakbayan 200417 ▁tanggal 200414 ୍ୱ 200403 দেশ 200402 ▁Đăng 200395 ▁män 200392 ▁միայն 200371 ▁ອາ 200368 ▁Dins 200360 vanja 200355 ▁레 200355 stat 200354 ▁igre 200354 ▁prodotti 200349 时候 200346 ▁interessant 200340 ▁lê 200337 kow 200336 ▁Nut 200333 ▁lấy 200326 ▁casu 200322 会议 200320 ▁Labels 200304 ▁αντί 200297 ▁waarin 200296 ກະ 200279 ▁លើ 200276 ▁ел 200269 alismo 200263 ▁yu 200232 ▁helst 200231 צות 200222 ▁zr 200218 ▁róż 200211 ▁liste 200207 chtig 200190 ▁Image 200184 ▁اقتصادی 200153 ▁iny 200149 licher 200141 ▁professor 200136 लेल्या 200123 ৈ 200103 ▁юу 200099 ▁Што 200096 agens 200089 ært 200087 ▁jednom 200079 гри 200076 രണം 200073 ▁giả 200066 דל 200059 ▁ón 200059 ▁erak 200057 ▁escri 200057 ುವುದು 200051 可能性 200036 ▁Mut 200031 ॥ 199998 дения 199985 ောင် 199971 ▁системы 199963 wit 199956 ▁edit 199951 ungo 199947 ▁достаточно 199944 ▁fels 199943 ▁città 199942 ▁Kak 199940 nicy 199932 ▁διε 199919 ▁iq 199910 ▁Калі 199910 ▁ጉዳይ 199900 ▁Quy 199895 cket 199887 ▁sehen 199886 ଦେ 199884 ըն 199874 ▁(12 199869 ▁məh 199868 もし 199868 ▁plaats 199865 ▁Она 199855 ▁DP 199845 cke 199842 ▁única 199840 ▁לכם 199840 ▁dünya 199838 ▁ಸೆ 199838 ύπ 199817 ▁यहाँ 199806 ರಲ್ಲಿ 199797 ▁regular 199793 ▁شان 199792 ▁dưỡng 199789 udet 199778 增加 199772 full 199771 čar 199767 ▁yapılan 199762 jest 199759 且 199750 ได้รับ 199737 ▁гэр 199734 ▁lupa 199728 ▁proiect 199725 ▁때문에 199717 세요 199716 ▁câ 199711 ▁Econom 199709 ▁사진 199709 цыю 199704 ▁cât 199686 팅 199676 ▁agres 199670 ▁پہنچ 199667 ▁нов 199661 ▁પુ 199661 ▁હવે 199643 ▁deixar 199640 ▁tán 199635 ▁panjang 199618 交通 199601 ▁đa 199596 ▁նախ 199586 ▁sic 199583 ▁đâu 199580 ▁anlam 199572 ▁cực 199571 דם 199567 術 199567 ikai 199561 ▁Lind 199561 ▁robh 199547 ▁beri 199546 omány 199545 什麼 199545 "- 199536 нуть 199532 ▁həyata 199515 ▁Sri 199506 ▁teman 199502 ▁‹ 199500 อั 199498 ▁sorte 199496 ovin 199493 ▁წელს 199493 ▁ката 199492 ▁ارائه 199489 глед 199479 stop 199473 ▁ASS 199465 ▁havia 199454 ▁miten 199453 ရွင္ 199448 ▁Finan 199445 ▁ਰਹੇ 199444 ▁kaže 199437 өд 199430 laz 199424 ▁pant 199421 ▁ಕೋ 199420 ives 199410 ارف 199409 ڈی 199405 வன் 199405 ึ 199404 ▁ෆ 199384 ▁ismer 199382 ภา 199377 ▁بیش 199371 ▁Funk 199366 ▁تكون 199347 ** 199343 ▁svenska 199320 定的 199316 ▁ಅಂತ 199314 रात 199311 ▁Há 199305 ?, 199285 ▁karar 199281 ▁aunque 199279 ▁manj 199279 ဟု 199277 ସର 199274 ▁duas 199268 െങ്കിലും 199266 誰 199258 ▁ලංකා 199257 ▁кад 199250 ▁wó 199247 ▁tương 199237 vana 199230 ້າຍ 199230 ▁biểu 199229 ▁Costa 199223 ▁ریاست 199211 ▁Nuk 199199 ▁ਲੈ 199194 ▁giữ 199177 inq 199175 älla 199173 之一 199167 ▁iliy 199158 ▁САД 199152 жать 199133 ตอน 199127 вір 199108 ▁باشند 199104 cea 199101 ących 199100 kara 199093 чого 199088 抗 199079 нев 199064 ▁mene 199057 ▁gă 199035 ▁bonne 199027 ▁पहु 199013 ▁trouver 198984 .07. 198973 ▁Nj 198972 ಜ್ 198963 ▁loogu 198959 itve 198946 ▁yếu 198936 ▁δημο 198920 ▁serikali 198877 ▁Así 198876 ເກ 198872 minist 198847 ▁mode 198834 ਨੇ 198831 ▁йо 198820 ▁mẫu 198815 شید 198807 ▁ਸਿ 198806 ▁độc 198805 ▁soy 198784 ▁التن 198778 ▁נא 198775 능 198772 ▁projet 198768 stup 198767 ▁парт 198747 ▁veure 198743 ນັ້ນ 198741 ▁darah 198740 ▁تون 198731 ଟର 198724 тных 198719 ▁erine 198711 ัญ 198706 ▁tine 198703 ▁বিভাগ 198694 строй 198692 ▁Бар 198661 ្ទ 198660 စီ 198647 ▁सन् 198643 ató 198642 bide 198642 ytu 198640 ватися 198640 ທໍາ 198630 ▁egész 198628 ▁diferents 198623 ▁POL 198622 хова 198611 ▁tuvo 198608 ေက 198598 ▁miq 198594 などの 198588 ತು 198581 ▁Θε 198580 ▁ኮ 198571 wić 198550 ▁предо 198548 ▁زم 198541 ▁ngaj 198537 ატი 198529 ೆಗೆ 198521 үл 198517 ▁donne 198516 ▁këto 198511 ▁Fryske 198486 ▁связи 198484 ▁მოს 198479 เตอร์ 198474 ▁കുട്ട 198467 容易 198466 关于 198451 tiden 198447 ▁avere 198447 ▁labing 198441 ▁bald 198433 שם 198426 พอ 198425 ▁5) 198419 ▁esit 198419 ▁prime 198401 ▁Loc 198392 ▁जाए 198389 ▁mateix 198384 ▁کاهش 198368 ▁kopi 198361 вной 198359 انت 198351 ō 198349 ਜੇ 198344 ▁teknologi 198339 βε 198333 ေပ 198333 ဓ 198327 ▁һи 198326 ျပီး 198317 oleh 198314 தே 198289 ▁reb 198281 ต่างๆ 198275 wali 198273 ène 198272 ▁Bản 198272 ▁работу 198268 ▁اڳ 198250 ▁തുട 198232 ▁체 198222 ämp 198214 ▁palīdz 198193 ▁parlar 198165 透 198164 ▁privind 198162 養 198159 ▁پل 198134 ▁ઝ 198130 ωμα 198125 ମାନେ 198118 ন্ত 198114 ▁لأن 198112 ▁después 198099 ▁nied 198099 ▁фор 198096 ▁momba 198095 ibr 198093 ▁medzi 198084 igin 198083 ▁akti 198083 ▁제품 198070 ▁nua 198066 ▁κάν 198063 ymo 198062 ▁regel 198057 ▁aquò 198031 ▁جاتا 198027 field 198021 ▁опреде 198016 ▁такие 197985 geme 197978 ▁විද් 197965 ▁Norte 197951 час 197947 ▁snart 197947 द्य 197946 ▁možete 197936 ચા 197927 ดํา 197927 ▁pew 197923 funa 197919 ▁الام 197918 ▁kwakho 197917 ▁κάνει 197916 ▁році 197914 ▁noq 197913 ▁isikhathi 197909 ▁rö 197906 цима 197900 ▁Deg 197895 æng 197891 ▁chú 197871 ▁преда 197868 ናል 197843 营 197835 있 197828 ▁транс 197827 ไร 197826 ▁მან 197826 ▁دریافت 197825 ▁īpaš 197816 ෙකු 197806 ▁ufuna 197805 entin 197802 бал 197799 ▁Fit 197799 ▁៖ 197790 ▁baza 197783 ▁کمک 197765 hj 197760 ▁Secret 197750 ▁мэдээ 197744 ▁kuwe 197743 ▁шинэ 197736 рами 197735 rana 197733 ▁ανά 197729 ▁tage 197716 ebenz 197715 энэ 197705 ▁timpul 197697 ▁پسند 197697 ▁പല 197682 ▁наших 197666 ▁Door 197658 ▁γεν 197647 ▁베 197641 पूर्ण 197629 ▁Sob 197625 aper 197623 ▁respect 197623 ▁hoop 197613 ▁Andre 197612 ▁запла 197609 ▁Good 197607 ▁Lagu 197606 ▁ري 197606 ▁Rusia 197605 ▁murah 197605 എസ് 197600 ▁kerül 197600 ▁especí 197597 ▁держа 197586 сах 197584 ▁Mä 197582 ▁kraju 197582 ▁lut 197578 ෂ් 197573 скія 197567 حی 197563 离 197558 ريق 197542 պան 197538 žių 197523 ▁tehdä 197522 ▁Botswana 197519 leng 197515 ▁مہ 197507 bí 197493 ▁mesti 197482 ▁puntos 197476 жете 197465 ຫຼາຍ 197463 ութ 197456 ▁भेट 197454 ▁treb 197453 oug 197445 indlela 197444 ▁иҫәп 197439 дау 197432 ▁lokale 197423 ಂಕ 197421 תיים 197414 ▁bloc 197406 ▁ධ 197404 ත්වය 197398 均 197378 ▁Санкт 197364 iya 197352 ოლო 197345 uluma 197341 ▁berlaku 197304 ▁зав 197303 zata 197282 ▁malee 197281 हरुको 197274 મો 197261 โก 197253 歩 197235 ▁kwam 197232 람 197231 ▁více 197223 ゴ 197216 ▁iza 197213 ▁محل 197210 ▁װ 197205 ▁tomar 197201 ▁ajan 197191 ▁पुलिस 197191 ▁פע 197171 ▁Daca 197169 ▁уй 197163 ▁لیا 197163 rika 197161 astra 197157 ▁એમ 197152 ▁gram 197147 śl 197144 ことで 197142 ▁xente 197133 UV 197129 ufu 197127 ότε 197123 گز 197117 ეკ 197110 laš 197104 ▁ndani 197099 šnje 197089 ▁хү 197082 ▁සිය 197079 andika 197078 ▁conver 197077 նելու 197072 ammen 197066 ▁partie 197042 гаш 197038 ▁cours 197036 ição 197026 ▁Kjo 197026 stell 197019 ▁Nabi 197000 ▁ಎಂದ 196999 ítása 196997 ρων 196995 мера 196987 ▁такое 196977 cido 196961 ▁consegu 196950 ▁ଉପରେ 196946 ▁cep 196935 ▁negativ 196926 ▁ולה 196923 ச்சு 196915 ▁Zah 196914 ▁okkar 196913 λόγ 196887 ▁hitta 196872 ▁тейеш 196871 ▁தா 196865 šť 196856 mac 196849 ▁koht 196849 测 196834 움 196834 orre 196827 ▁suma 196817 ▁Dha 196816 ומים 196815 скіх 196814 ồn 196811 ▁entrar 196810 ▁gratu 196810 ▁realmente 196788 ▁Thanh 196780 voir 196778 ために 196773 ▁ਸਮ 196769 ▁(← 196766 ▁especialmente 196763 ടുത്ത 196756 etzen 196755 ▁அது 196753 ▁ਵਾਲੇ 196745 ▁éves 196744 اران 196730 ▁estim 196715 ▁kamer 196696 领域 196689 nije 196676 йма 196667 куль 196666 ▁ţ 196666 бом 196656 លើ 196653 ▁stund 196645 ▁അനു 196644 veis 196641 еха 196638 ▁tưởng 196634 artha 196629 ▁warga 196625 技術 196624 erik 196619 ▁техника 196618 жин 196617 ▁3-4 196614 embro 196613 มิ 196612 ▁Ell 196607 しても 196590 ญา 196588 ▁sieglu 196584 ▁Bagi 196583 ugs 196581 電話 196580 โอ 196572 5- 196566 ests 196562 ▁zl 196552 Ра 196546 ▁različ 196546 ിക്കുന്നു 196543 ▁Dist 196521 ▁mezi 196520 éta 196504 ▁ғә 196479 ▁груп 196478 amit 196474 個人 196472 ▁aprove 196468 скім 196467 射 196446 :45 196445 istus 196444 ที่ดี 196437 alos 196433 ▁nguNkulunkulu 196432 ▁geme 196431 ▁لیگ 196428 ▁paket 196427 pop 196425 пат 196420 ▁doit 196414 ▁geldi 196414 ฤ 196394 接受 196392 werp 196379 ▁ਗੁਰੂ 196377 ▁event 196376 ugt 196375 ▁حسین 196373 ጃ 196369 νά 196359 viv 196355 ▁nguồn 196353 ▁ബോ 196346 觀 196346 ▁राष्ट्र 196342 inha 196340 unik 196337 ▁충 196335 ▁Sø 196323 ▁kilka 196317 遇 196316 bab 196313 ▁declara 196306 isar 196291 сії 196282 ▁האם 196257 ▁klart 196256 yil 196255 ▁Ivan 196251 :12 196245 ▁Jugend 196244 ▁ame 196243 enii 196240 ▁savoir 196229 ▁resulta 196225 Со 196211 ▁stal 196184 ▁सर् 196182 ▁annan 196175 ▁jah 196166 ▁मध्य 196166 ▁chez 196156 ▁σύ 196148 ▁НЕ 196145 zech 196140 ▁мисли 196122 ▁چا 196119 ಾಂ 196115 üy 196108 ▁tém 196102 ▁նրա 196101 ▁cliente 196094 ▁звер 196086 ▁אזוי 196085 kri 196081 ▁다양한 196078 ण्यासाठी 196062 ▁दिए 196057 ▁exclus 196056 ▁ሁኔታ 196040 aké 196039 ▁หน้า 196039 ▁uray 196035 พร 196034 UE 196029 сийн 196027 oof 196025 oe 196020 нача 196016 ▁utiliza 196014 ▁articol 196011 ải 196009 ▁perfect 195993 ▁ព្រះ 195993 ▁הרב 195992 ▁stvar 195991 ierte 195987 שו 195981 ▁llevar 195967 穿 195966 5] 195954 ▁agli 195951 бат 195949 ▁çək 195947 ències 195913 ▁tieto 195884 ▁Dës 195870 ▁concep 195856 mið 195849 ances 195841 ▁кезде 195832 ▁государствен 195831 Ph 195822 ▁Ł 195812 ţiei 195804 ▁وقال 195799 ▁каждый 195793 İL 195787 ▁black 195776 र्न 195774 ạch 195771 meg 195766 ▁فإن 195766 amia 195761 ▁bist 195761 ▁atra 195753 ihi 195752 össä 195737 ából 195731 ▁තිය 195730 พบ 195726 ▁منها 195715 ēta 195714 noc 195710 ETA 195703 amus 195700 ▁slag 195699 ▁buy 195686 议 195684 ▁cadrul 195680 mah 195672 ▁சொல்ல 195671 ▁ଆଜି 195669 ygg 195667 рем 195633 構 195621 ▁lepas 195607 細 195600 ▁склада 195594 ▁пита 195592 ▁ادا 195582 ▁mys 195574 ▁consci 195565 ▁אתה 195563 lumot 195554 ▁vib 195542 шую 195539 ▁Komis 195539 ▁грн 195530 ▁rendszer 195526 ▁үтә 195525 ▁ngành 195523 žiť 195522 ▁česk 195509 ▁possibil 195503 Ви 195490 ▁кило 195488 最近 195486 ൂര് 195472 ▁সি 195469 AI 195467 EU 195458 ื 195457 ▁Pier 195457 alist 195456 ▁дуу 195451 ڄ 195444 น้อย 195444 ▁ڄ 195437 ▁консу 195427 ovar 195422 rī 195422 િવ 195408 ▁खबर 195404 ▁ሙ 195392 dua 195386 ▁رب 195377 ▁hawa 195376 ▁jie 195373 ▁formación 195365 ох 195359 ▁રે 195349 ோம் 195332 fle 195331 ▁ಕೈ 195326 ▁emple 195323 anter 195322 ▁ನೀ 195321 ▁redes 195317 ▁completa 195307 ್ರೆ 195306 ▁Nes 195303 ▁català 195297 ▁gast 195283 ▁estrat 195270 ▁юриди 195266 ▁kiếm 195249 вра 195248 рія 195240 ▁развития 195231 iausi 195222 วี 195221 ▁Lor 195221 ֹ 195212 ძი 195207 န္႔ 195201 ▁відкри 195196 iting 195194 ▁бич 195178 ▁यांच्या 195170 ▁Pur 195168 ▁thotë 195163 ▁حک 195160 ▁shp 195148 лаш 195146 արձ 195145 ▁akadem 195144 ▁աշխատանք 195133 ▁Papa 195111 ▁ping 195098 ▁kukhona 195061 ▁веб 195059 ▁ating 195058 ▁тура 195054 сля 195051 ▁kust 195050 ▁Rome 195042 ▁معلوم 195016 ੜੇ 195008 ▁גדול 195008 ▁urma 195002 ▁رکھ 194998 ▁estes 194996 ▁llugar 194992 ställ 194960 ▁منطقه 194943 ථ 194940 ▁ប៉ 194938 ण्यात 194932 ▁Terbaru 194932 ▁strat 194927 ▁உட 194927 ▁Fri 194926 ▁resultado 194906 ικο 194900 nými 194899 ▁1930 194899 ▁milf 194898 ▁bizi 194888 sie 194887 ▁mož 194877 vant 194873 ▁конферен 194860 ▁ngum 194857 ▁186 194856 ▁schw 194854 ▁VE 194845 ▁groep 194843 ariat 194836 ૯ 194828 割 194828 קומען 194822 ระดับ 194819 ▁СССР 194816 ăt 194812 ▁distr 194809 ▁እስከ 194807 ဒီ 194802 āciju 194784 irii 194781 ▁вона 194770 ▁सभा 194768 ▁여행 194754 terne 194752 باد 194749 ête 194742 голо 194741 ▁Rich 194738 ▁sāk 194738 ▁đá 194738 ▁Zá 194728 αιο 194721 ន្ល 194715 ነው 194711 なくな 194711 wez 194709 ▁Bio 194708 λλα 194707 園 194707 ▁შეს 194705 ▁fikir 194699 zeg 194694 ▁biasanya 194691 ▁oblik 194690 מין 194680 ▁kondado 194680 ▁Cette 194668 ▁sentido 194663 ▁Евро 194646 ▁онд 194633 ▁случа 194633 ▁다시 194626 ▁ende 194611 حات 194604 ंध 194601 ▁mgbe 194596 θα 194594 ▁volunt 194583 ▁musí 194573 tayo 194571 לות 194568 ▁гэх 194568 ▁مهر 194562 ▁tunt 194558 ▁koncert 194545 ▁Gör 194543 ekkel 194534 .; 194531 siz 194531 ▁saha 194526 ▁देखि 194519 ▁Seite 194518 ▁لر 194517 ▁ولسمشر 194517 ▁फे 194515 ▁ndee 194501 ພະ 194495 ▁चाह 194492 ভাবে 194487 ▁virun 194479 менты 194470 cent 194467 ▁있어 194462 ▁styl 194439 ▁الذين 194438 yə 194431 ▁Borg 194428 ▁estaba 194420 lh 194419 ▁صوب 194413 ▁ура 194410 рін 194386 ▁باب 194385 ▁Læs 194365 ▁Све 194364 ▁ማለት 194355 ▁гэ 194352 ▁peg 194342 iria 194338 ▁нийт 194334 sche 194331 ંક 194318 ilit 194294 ▁otr 194288 кор 194285 ▁Institut 194285 ▁കട 194277 ▁minimum 194268 ▁Negara 194251 ▁pse 194250 ▁авах 194245 ▁anpil 194241 ▁drejt 194238 улга 194237 ▁menerima 194232 ▁erk 194231 표 194229 ▁පිට 194225 ▁sost 194224 ▁Их 194216 ▁дней 194215 ▁luc 194207 ▁merasa 194207 ▁Lietuvoje 194202 ▁busca 194195 раг 194189 يمة 194189 领导 194186 章 194177 ▁방법 194175 ▁애 194174 פשר 194170 ▁нешто 194159 ഹി 194151 міна 194147 ▁Пар 194146 ▁фести 194119 ▁hava 194103 ság 194089 ryf 194071 ▁rete 194061 mond 194052 ▁Ау 194051 ▁שהוא 194047 bei 194043 बु 194041 気が 194041 ▁በላይ 194016 empat 194011 ikti 194010 أن 194009 ▁вз 194005 ▁اقتصاد 194001 ▁aantal 193996 ▁okuk 193995 ▁dala 193990 нама 193989 ▁mikä 193989 زن 193987 ▁Christian 193984 ▁love 193967 ▁tied 193962 ▁përdor 193953 sub 193948 建立 193947 צוע 193943 ंतर 193941 ▁plusieurs 193936 ▁ຕໍ່ 193934 ▁cynnwys 193907 ▁187 193903 ▁Hvad 193898 щото 193889 龙 193881 ▁siin 193876 ▁była 193871 ഘ 193868 ▁diay 193865 ציה 193860 ੋਰ 193856 बल 193848 ▁cân 193842 ▁vlo 193834 ▁ಮೂಲಕ 193832 ▁hob 193830 ▁газрын 193823 جز 193822 ▁получить 193822 ▁Aktu 193820 ỉ 193800 ▁Sai 193800 ▁числе 193793 需 193793 пак 193792 obile 193786 ▁ራ 193785 ▁потре 193782 për 193750 ”) 193742 ▁تھ 193724 ▁ලෝක 193719 تاب 193696 īgu 193694 ▁lamun 193694 黄 193682 ▁genau 193677 ▁flash 193669 ALE 193668 势 193655 ▁առաջին 193642 ▁لوم 193636 ▁Camb 193632 ▁glav 193631 ებთ 193627 ellet 193614 ▁Chaidh 193613 ▁tyr 193609 ▁kent 193606 ▁amerikan 193604 önd 193600 ্ল 193596 சா 193589 ▁již 193586 -09 193581 ֵ 193577 उँ 193577 ▁lod 193571 હી 193570 ▁пле 193567 得到 193566 zato 193560 ▁Transport 193556 ▁kayat 193542 ▁جانے 193532 ▁често 193523 ▁Presiden 193514 ▁statt 193502 ▁neat 193494 ▁trobar 193493 ▁Dies 193490 tud 193487 ▁9- 193486 ▁объ 193470 ▁rank 193469 झा 193468 ությունն 193462 ▁بك 193458 ୁର 193450 پا 193441 дня 193434 big 193431 ▁सार्वजनिक 193414 ▁menyebabkan 193411 ▁deth 193410 ▁цели 193408 ▁dada 193400 ▁عبر 193392 UF 193386 ▁ira 193386 ▁während 193381 ექტ 193375 ▁ilin 193361 ▁Той 193360 quel 193344 ▁Latvijā 193344 צר 193339 ▁tež 193338 ▁Mayo 193331 കാ 193325 വം 193323 tific 193322 ▁Upp 193319 anije 193312 ▁поме 193299 ▁ziy 193298 ▁interesse 193288 צל 193287 ▁nghe 193283 чини 193279 ▁Chu 193277 ▁տարբեր 193274 ▁nghĩa 193270 ▁produtos 193266 anim 193265 ▁jusqu 193265 ည် 193240 ▁لے 193234 ▁mena 193227 亮 193215 ▁ਦਰ 193211 盛 193208 ിലും 193194 ▁protagon 193186 ▁Ged 193179 ▁servir 193177 ▁земе 193177 ivät 193156 atuta 193154 niecības 193138 menta 193135 ▁ખા 193134 双 193134 лок 193130 ▁waj 193124 ▁figura 193122 šče 193121 ុស 193112 ▁ਜੇ 193111 ▁svært 193105 /2016 193087 ▁대해 193072 торы 193069 ชาติ 193056 Ро 193047 ▁abad 193047 ēl 193041 ▁සිදුව 193039 сылар 193035 ющей 193029 ▁12- 193012 ▁avt 193008 ▁mach 193006 ريف 192992 ਸ਼ੀ 192986 ିର 192973 ▁علوم 192918 itul 192911 ▁پرداخت 192910 ▁pòt 192909 ▁mesmu 192903 ▁נפ 192888 ▁Lauren 192886 ่าย 192874 ▁decis 192868 ▁пайда 192866 身体 192866 ▁окру 192863 ▁सहयोग 192862 était 192861 TU 192841 ▁szolgáltat 192839 ILL 192837 ▁Sony 192835 ęt 192822 ▁Jose 192817 ▁(11) 192816 astre 192805 צן 192800 跑 192799 ıda 192781 ▁רח 192778 ▁اسی 192776 ומה 192774 ington 192769 ▁აქვს 192747 FR 192743 ▁berg 192742 さんの 192742 ▁pove 192713 лити 192711 ▁Def 192711 ▁kanal 192708 ▁சிற 192708 lament 192707 ▁ahal 192705 hav 192700 پار 192699 ▁byt 192689 iyy 192678 ▁Пла 192668 wụ 192666 чнай 192655 ▁פרא 192654 ▁klase 192642 ಠ 192636 这么 192635 ▁menang 192614 ւ 192599 ▁בנ 192586 ▁ਏ 192581 萬 192578 ▁دوی 192560 ▁lad 192555 ▁ಇದು 192539 苏 192537 Vi 192521 цаў 192504 lil 192500 ▁asemenea 192492 ▁වෙන්න 192485 ▁няколко 192477 ▁माहिती 192473 νων 192469 ▁København 192469 ▁स्वास्थ्य 192450 erson 192445 ▁Lond 192433 ▁ฟ 192433 osk 192431 ▁එක්ක 192426 ▁технологи 192423 ▁अर्थ 192423 ▁Mən 192420 ▁মু 192409 ▁또한 192409 zení 192397 ▁suure 192394 ▁budou 192391 ât 192379 ▁mindig 192376 ائون 192375 ▁हाल 192370 tter 192360 ▁spi 192350 બી 192334 ▁konto 192315 ▁חלק 192311 alisme 192306 cab 192305 ▁támogat 192304 ▁hlad 192301 [1], 192297 ▁систе 192291 гом 192275 зам 192274 ▁baino 192271 ▁maw 192270 ▁landet 192260 ▁Під 192256 ▁රිය 192252 ▁Јо 192239 ▁MAT 192234 ▁годзе 192229 ucht 192227 ▁कई 192227 ▁technolog 192220 how 192219 ▁малко 192218 ▁منذ 192218 ▁Авто 192217 date 192206 ▁certo 192201 ▁Will 192191 ▁ministr 192191 dei 192173 ም፡፡ 192148 洋 192136 ▁nooit 192128 ▁základ 192121 ▁pys 192118 자가 192117 kelijk 192116 ندگان 192113 ▁שכ 192110 egur 192104 규 192102 ည့္ 192100 ▁pelos 192090 ▁lucr 192071 ▁փորձ 192071 ▁photo 192069 ▁قدم 192063 ▁tw 192055 ▁maliit 192052 jenje 192050 utsh 192050 ▁қай 192042 ඩු 192035 ▁Cod 192034 askan 192025 ▁sider 192020 ▁sådan 192014 sst 192007 الب 192001 ុប 192001 ▁druk 192001 ▁imi 191999 ▁cinta 191998 στά 191997 ▁Firma 191973 сний 191962 ▁प्रश्न 191961 ▁talde 191953 ▁venn 191950 ▁напа 191943 ▁national 191936 ▁travay 191936 ▁பின் 191918 ▁loba 191917 cionales 191909 ▁faktisk 191892 ▁درخواست 191892 ଟେ 191891 ▁videos 191876 ▁קאַ 191865 ▁ພະ 191861 ளு 191860 ார்கள் 191856 ▁изпъл 191853 ▁danh 191844 ▁gn 191844 brig 191840 ▁angen 191833 ▁unser 191830 ▁것으로 191826 ▁המת 191824 çu 191822 ▁dw 191819 ▁pagar 191815 ▁расска 191806 alizar 191802 ▁Dihlatse 191801 ▁musica 191798 ▁Қазақ 191788 ▁nichts 191772 單 191771 ▁razvoj 191762 ▁ráð 191755 ▁كن 191725 ▁milij 191721 র্ষ 191713 дии 191697 ВЕ 191672 ▁አብ 191666 ▁بک 191664 ▁რუს 191663 ▁былі 191661 ację 191654 pens 191653 ումների 191638 ▁Hän 191638 ▁anno 191631 ▁nëmmen 191629 ▁ابن 191623 ▁Led 191622 ▁obč 191616 服務 191613 ▁prova 191607 ▁वर् 191601 ▁ಅದ 191596 ▁kyl 191590 ▁būtų 191588 기도 191588 ▁Ris 191586 ▁DJ 191584 ھي 191573 née 191555 )... 191552 واد 191552 ▁կապ 191549 岁 191543 ▁Nhật 191542 ▁уны 191542 ▁Ess 191519 ▁наме 191517 ▁kojem 191516 ▁stati 191516 題 191488 एर 191473 ៍ 191463 ▁وجہ 191450 ▁Він 191445 DF 191441 ▁abbiamo 191441 ▁लेकर 191440 ▁sesuatu 191437 adne 191435 ▁czasu 191432 ▁gemaakt 191430 qh 191424 ▁Oromiyaa 191422 erad 191416 ▁زموږ 191407 alih 191391 ▁ida 191388 ▁Manchester 191377 ▁Rhannu 191372 atív 191367 ▁rand 191362 wadi 191358 ▁सय 191344 ▁Baz 191343 èlh 191332 ▁sampun 191328 ಾಯಿ 191325 ▁Studio 191321 ▁செய்திகள் 191317 ▁Российской 191313 Да 191300 ▁nato 191298 ága 191294 ▁خارج 191289 dende 191288 ▁есте 191286 лых 191271 ▁compan 191270 ▁előtt 191270 是否 191262 rast 191259 ▁unga 191259 ▁отра 191255 ▁lenn 191243 ▁басқа 191236 อยู่ใน 191234 ▁kinne 191226 ôt 191222 ▁Gas 191221 ▁cú 191216 ხე 191196 ਨਾਂ 191184 መለ 191179 ▁organización 191172 မှု 191165 ▁સો 191163 ++ 191158 жных 191157 ਸਟ 191157 фу 191149 ▁Januar 191136 水平 191124 udas 191118 ▁ટે 191109 ekela 191102 aketa 191099 ▁ട്ര 191099 μμα 191093 ▁клю 191093 ເມືອງ 191085 দিন 191084 ▁۱ 191072 ▁mein 191070 ëz 191069 ▁finale 191057 ▁dział 191053 ล์ 191013 ▁ilmu 191009 ▁ทั้ง 190988 ▁Isi 190966 тарға 190964 paus 190960 ▁perso 190959 wir 190943 ▁Fach 190940 ▁nalika 190932 ▁etish 190931 ▁konst 190925 च् 190900 ေၾကာင္း 190891 ▁Rò 190883 try 190877 ▁Hus 190877 ▁직 190848 ▁1.0 190844 шан 190842 ▁이번 190838 ▁kast 190834 ▁bred 190829 ▁cụ 190824 amn 190823 ▁بيان 190801 irme 190793 δή 190774 ▁kommentarer 190772 협 190772 ▁ընկեր 190771 ▁Гэта 190769 irte 190768 ському 190768 ុន 190765 мам 190757 ▁aihe 190753 ▁serà 190747 ▁cazul 190742 ▁kanilang 190737 дон 190736 ▁хүс 190733 cular 190727 ▁jautāj 190725 ▁mapa 190716 ▁client 190710 áló 190704 ▁ନୂଆ 190703 ▁هی 190700 जु 190695 ukseen 190693 ▁card 190690 ▁luni 190685 orea 190683 ēd 190681 ▁Auss 190680 ▁Publ 190677 ▁ලිය 190677 ▁viện 190668 къ 190667 ▁לך 190666 ▁kaliwatan 190665 ขา 190662 ▁तु 190660 ▁Suk 190653 ეთი 190652 ecz 190645 ዎችን 190642 ▁فوج 190641 ▁தலை 190641 ▁आइ 190639 աժ 190631 ▁سل 190627 igkeit 190620 ▁ಹು 190610 hend 190594 只要 190589 തും 190588 견 190581 ▁yas 190573 睡 190573 ▁definitiv 190566 ▁spus 190561 Da 190554 yana 190551 wwer 190546 etni 190533 ткән 190532 таль 190530 nite 190526 قاء 190524 ▁Ζ 190521 ▁thất 190519 පි 190518 ▁kuidas 190511 ▁бөгөөд 190504 ▁stå 190500 æld 190497 োন 190496 ▁iga 190495 ▁funktion 190494 ęcz 190490 ▁Չ 190487 ▁કામ 190472 ▁کوم 190461 ▁všetky 190457 ੱਕ 190454 ▁učen 190451 ▁cambio 190442 ▁grouss 190437 ▁TripAdvisor 190436 았다 190434 aval 190433 ▁атрыма 190430 дже 190416 ूत 190409 új 190403 吸 190393 ▁որոնք 190391 ▁кое 190378 ▁ორ 190378 ▁ቁ 190378 ▁بگ 190376 été 190375 чий 190369 ▁qaar 190364 ▁raya 190359 ▁одной 190359 АЛ 190332 jeni 190331 ▁קענען 190315 ירן 190285 قف 190279 ▁visas 190248 muş 190246 ▁Hvordan 190246 :19 190233 ▁คือ 190229 ▁waz 190203 டைய 190200 ▁aument 190200 ▁բայց 190199 ැයි 190198 ▁adop 190198 ▁ծառայ 190189 agn 190169 ▁ጥያቄ 190162 ▁tali 190160 ្តី 190153 ▁Chat 190152 ITE 190146 ਯ 190142 ▁اغ 190141 ▁MIN 190140 ▁Acc 190114 ▁ללא 190109 ▁back 190091 oek 190088 ▁बोल 190088 関係 190086 ंगे 190083 uvchi 190081 ▁Spiel 190076 ▁yal 190075 ▁কাজ 190072 ოკ 190066 ▁оде 190062 unti 190055 јем 190048 obili 190047 фия 190045 erti 190044 ▁Phetolelo 190040 ertura 190039 ▁الأول 190037 ▁đấu 190028 aknya 190025 نى 190025 ▁dëst 190020 pression 190012 ▁번 190011 েট 189992 ጊ 189990 ▁ແຕ່ 189987 ржа 189967 дума 189966 ▁đoàn 189965 ელს 189947 ▁κυ 189946 ▁cuối 189937 ▁basi 189934 မြ 189921 ▁Є 189917 ▁պաշտոն 189914 sah 189911 ▁potrebu 189911 adoù 189897 вува 189897 mist 189893 ▁محفوظ 189891 ecen 189887 ્રે 189862 чын 189860 інде 189845 播 189844 ▁gip 189831 ▁شہ 189825 ▁ƙ 189813 still 189808 ВИ 189806 ▁Ov 189806 :14 189797 ▁лично 189795 ્ટ 189774 あり 189763 ▁обви 189756 immo 189753 ▁behandling 189750 发布 189744 ▁بالت 189740 ▁ด้วย 189739 ▁dialog 189735 리를 189729 ▁прош 189719 बो 189715 uwe 189694 ▁adapt 189694 ▁materiale 189690 ▁sodel 189688 стрі 189678 ▁qan 189672 ▁formas 189658 quv 189641 提升 189639 ▁kono 189630 ▁લી 189630 폰 189629 ▁инстру 189627 ▁യ 189624 ▁التع 189604 ▁zure 189603 ▁kubwa 189595 მარ 189589 ▁Már 189584 いただ 189577 ▁bruge 189570 မည္ 189567 ▁holde 189563 ▁ਕੁ 189561 ▁ଆଉ 189559 чество 189556 ▁पहिलो 189555 ▁ici 189554 ဒါ 189540 ўны 189538 ▁وري 189534 ▁ਗਈ 189527 ▁ਜਾਣ 189521 viä 189515 ሉት 189507 ០០ 189506 ▁babae 189500 ▁آی 189499 ▁상품 189495 ▁laut 189492 ▁мэдээлэл 189486 redit 189482 रु 189468 ▁ዳ 189462 роди 189460 ▁անգամ 189454 ▁ማስ 189450 許 189449 ▁unay 189448 ▁Cong 189440 ▁MAG 189436 ▁awak 189435 ēšanas 189426 ▁իմ 189424 ▁vuonna 189408 を行 189403 ▁ειδ 189396 ന്റ് 189393 ▁maja 189384 ▁diff 189383 ▁Glas 189381 אַס 189378 ▁Besch 189374 нка 189373 ▁включа 189373 DB 189365 สําคัญ 189358 ▁될 189356 ▁muzik 189352 гледа 189351 ▁cím 189345 ▁کننده 189341 diy 189332 ▁таму 189330 τούν 189313 ▁Lop 189303 ▁conseguir 189302 ▁ຫນ 189298 ▁Tres 189295 ▁zawsze 189285 ▁bangsa 189281 ▁bub 189280 lm 189277 ▁bobl 189271 ▁bóng 189270 ležit 189268 ▁кафе 189267 iyada 189259 ▁պատմ 189243 ▁필요 189231 ự 189217 ▁Sarajevo 189214 ▁siap 189208 ရေး 189204 endelea 189199 tivos 189195 ▁кръ 189194 ▁ater 189193 ▁mediante 189193 /8 189191 มน 189191 γο 189183 ▁Је 189182 edá 189174 ajat 189172 φορά 189170 ▁چاہ 189150 ▁ló 189148 ▁Ikke 189144 メール 189130 ▁yiziphi 189128 রি 189120 ▁хоёр 189114 ▁alkalmaz 189113 rú 189109 ▁сна 189104 ▁곳 189104 ipp 189099 cit 189095 ្គ 189094 ▁אחר 189090 ▁reikia 189086 ▁Ray 189083 ▁suunn 189054 obal 189043 ▁attività 189036 ▁визна 189030 ▁Федерации 189029 úk 189015 ày 189012 ፈል 189004 юсь 189003 ペ 188979 ိုင္ 188954 ▁чего 188950 ը՝ 188929 ใด 188926 ▁kroz 188918 ▁ngad 188916 ▁tamu 188915 ▁करण्यात 188915 สิ่ง 188912 ▁उपलब्ध 188898 ડે 188890 ▁ет 188890 umel 188888 nehmen 188885 vala 188884 zeb 188882 iyyəti 188881 တီ 188880 DD 188877 ▁سیم 188873 ▁fro 188862 ▁Jangan 188860 ▁अम 188857 ▁zás 188854 ▁১ 188853 ļas 188851 ovni 188838 ">< 188831 ▁म्हणजे 188831 ▁wielu 188824 ▁Pend 188812 வம் 188809 ▁tämän 188800 費 188790 ▁leuk 188785 ▁Tang 188782 ▁ಶ್ರೀ 188780 ներն 188775 ▁χρή 188760 cimento 188753 ▁dood 188753 ▁uts 188745 ▁qualche 188733 ▁agric 188732 ▁jurid 188711 ▁этих 188709 ▁برخه 188700 orian 188692 daş 188671 ▁konz 188661 ▁kompon 188637 tej 188634 ▁Premier 188632 ไล 188617 дээ 188607 γη 188594 ▁taal 188594 ାସ 188581 ▁dispon 188570 ურის 188563 ▁idé 188561 UI 188560 ▁sơ 188549 ିବାକୁ 188536 ิส 188536 ▁eksempel 188536 ▁zac 188528 欧 188523 mynd 188518 ▁Ż 188506 தாக 188500 ګر 188480 գր 188474 ▁alder 188465 ▁زمینه 188460 ροι 188455 केको 188455 ▁Key 188451 ▁avons 188447 aus 188441 ▁bekannt 188437 rb 188432 जर 188429 ▁इत 188420 ▁huyo 188418 ▁dëser 188417 ural 188416 ▁Bere 188411 עלע 188400 asho 188394 ▁lamang 188388 ▁Dacă 188387 ▁wakhe 188385 幕 188383 גיע 188382 დით 188382 ፓ 188376 ▁rež 188374 ▁panags 188373 jš 188362 ▁Salah 188354 ▁болж 188347 νω 188346 ▁edilib 188341 ▁revista 188334 のに 188333 ക്കാന് 188331 ▁queda 188328 τσ 188316 кими 188282 னு 188277 ▁तुम 188275 annde 188273 ▁Standard 188270 वत 188267 dej 188256 ▁dzięki 188251 ▁часу 188251 зация 188232 ตอบ 188231 ▁діяльності 188222 ▁வர 188217 ▁يد 188214 ▁ጉ 188212 ▁امکان 188199 ପ୍ର 188195 мни 188188 શો 188186 osis 188185 ▁समेत 188182 ▁unut 188181 ចូល 188179 նչ 188173 вале 188167 ▁відповіда 188166 كد 188158 ଳରେ 188155 ▁private 188154 onnen 188143 ▁sola 188141 ▁yes 188141 ▁بانک 188128 ვირ 188116 ▁طول 188111 ▁bulunan 188108 غل 188107 wani 188102 ੈਕ 188102 ▁splet 188099 ▁موس 188099 两个 188094 TH 188093 тех 188080 ▁თქვენ 188076 agin 188069 ▁ફો 188057 ▁Είναι 188056 rata 188054 ▁שח 188052 ▁sklep 188048 ▁හැම 188039 bú 188037 مين 188025 ▁skat 188020 ▁čia 188018 agos 188017 ▁۲۰۱ 188016 aska 188012 ▁আপনার 187992 2.0 187979 ▁fär 187979 ▁ସ୍ୱ 187976 เพียง 187966 ▁oameni 187964 ▁недоста 187954 šno 187948 番 187919 ▁phù 187916 ಂಚ 187913 ▁ун 187910 BL 187900 ▁поса 187887 ▁Korea 187883 ▁කටයුතු 187859 fond 187853 gli 187851 ▁মধ্যে 187847 ▁szab 187842 rän 187826 ▁Lëtzebuergesch 187826 ▁Comment 187823 /9 187822 ▁11- 187818 ▁ڪندي 187817 èls 187815 ▁destac 187812 ▁носи 187808 ၆ 187803 wała 187798 ▁ਕੋਈ 187795 рена 187794 ANO 187790 ருக்கு 187783 ▁açıq 187781 ▁wet 187781 ▁бре 187778 ▁poskyt 187771 ัว 187747 uger 187746 ▁Koh 187744 ື່ອນ 187730 ▁جامعه 187730 ▁iawn 187706 ▁வெளிய 187706 ▁Кан 187700 ▁ژوند 187698 ▁przypadku 187689 ▁ding 187686 ▁fler 187686 ▁ακ 187684 ▁Доб 187678 ▁Roll 187670 ▁support 187666 ▁Hop 187665 ▁chom 187658 דור 187651 ▁aid 187649 순 187640 ▁апо 187638 ▁cra 187636 ▁onların 187636 ▁ank 187625 領 187622 ▁yosh 187618 ▁مم 187615 ▁bruke 187611 ▁gee 187602 ▁распо 187600 går 187595 óż 187588 لاح 187586 ေသ 187581 ▁آئ 187578 fyr 187570 ▁boste 187560 ycznych 187555 ▁Cand 187549 ▁зүй 187547 kép 187543 ▁Hist 187539 leid 187536 ▁ڪر 187498 ▁бес 187478 네요 187472 ရိ 187466 ▁hasi 187451 മാന 187450 ettet 187448 ▁نس 187446 ▁bosh 187438 ▁прија 187430 Go 187428 ▁شاہ 187422 emek 187418 ▁केन्द्र 187410 ંત 187403 stö 187391 ▁ગુ 187389 ▁kuu 187377 ▁turn 187355 emand 187354 ▁viena 187352 ▁клі 187349 yev 187346 ▁pô 187334 ஷ் 187327 Ja 187321 ▁البي 187320 Β 187314 ▁үҫ 187306 ▁näm 187304 ▁पह 187296 ▁dair 187294 ▁toi 187289 ▁ид 187288 ിൽ 187286 ▁талап 187283 ▁kaki 187273 ▁چی 187263 才能 187258 ▁Jong 187255 ▁боле 187255 ونکي 187249 ▁ойошторо 187238 细 187233 mitt 187226 ▁सूचना 187225 开发 187225 lərini 187210 ▁glede 187198 ற்ப 187197 ▁resep 187193 ▁tomonidan 187185 ▁داسې 187178 标准 187176 ▁кам 187169 户 187166 最高 187146 ▁ತಮ್ಮ 187143 額 187143 ▁személy 187135 ০০ 187123 еру 187102 ▁ਮੰਤਰੀ 187101 ូង 187090 jela 187087 ▁бей 187086 ▁Samo 187080 ▁সরকার 187074 erci 187071 ोड 187067 ▁Olen 187067 contr 187064 תוח 187063 ▁dort 187058 以前 187055 ▁გას 187036 ▁aast 187027 ▁modul 187020 ▁Job 187015 haz 187012 аны 187010 ēts 187003 ▁présent 187002 âl 186997 ▁PRO 186988 рына 186985 700 186976 ▁일본 186970 ▁Gob 186967 ▁FOTO 186966 ੋਲ 186955 apor 186948 روز 186947 ▁ging 186941 PDF 186939 ឡើង 186937 ▁warto 186925 ምን 186924 ტერ 186918 NC 186903 ▁डे 186900 ▁течение 186893 igos 186887 δέ 186882 reach 186878 antal 186867 ▁konuş 186864 ▁հան 186861 zav 186858 ▁තුළ 186854 ▁күн 186849 ▁найти 186847 ▁nhw 186836 ▁Kart 186835 ▁Conf 186831 ظم 186814 ▁Arbe 186799 рио 186796 нице 186791 usal 186775 ▁105 186774 ▁সঙ্গে 186771 ▁ಈಗ 186769 ▁ເຈົ້າ 186769 ▁клиен 186767 ▁гэдэг 186761 ▁hrá 186757 fet 186729 ▁ຫຼ 186724 ▁ენ 186723 ▁គេ 186723 лдер 186715 ▁इसके 186703 ▁posl 186695 吉 186675 ▁scen 186670 ▁pab 186669 ቅር 186668 ▁തോ 186662 ▁සමාජ 186661 ▁ivez 186659 бург 186658 ▁ευρώ 186657 чей 186647 cking 186643 isuuden 186637 ▁Indi 186634 ▁Puntland 186630 ▁feito 186623 ▁alguna 186616 ግል 186611 ▁беренсе 186611 ▁XVI 186600 ▁sais 186594 ▁დრო 186593 ▁담 186590 ▁κοι 186585 ▁ích 186581 ▁አገ 186567 ▁சில 186562 ▁ўсё 186559 ▁honetan 186554 ▁حڪومت 186552 ತ್ರಿ 186550 ▁Mutta 186541 kā 186536 ძე 186532 žel 186525 ാനും 186522 ▁прили 186522 lein 186516 वान 186508 ▁IB 186506 ഗ് 186505 ▁شاهه 186498 kre 186497 ject 186493 ▁उन्होंने 186482 抱 186481 ragen 186480 相談 186468 ▁Menurut 186466 ▁সময় 186463 ▁wneud 186456 ▁πριν 186445 ▁descon 186436 ▁cav 186434 ▁País 186432 ▁lätt 186429 ▁fle 186425 ▁230 186419 ▁againn 186419 ிருந்த 186416 ▁tig 186404 ▁вой 186404 ▁Pf 186401 izat 186400 ▁산 186397 比較 186393 မှာ 186391 ezza 186386 ▁wollen 186383 ▁خدمت 186381 ryt 186375 πέ 186371 ғаны 186365 ▁parasites 186362 ▁게 186350 ርያ 186343 ▁անց 186342 ▁플 186341 애 186340 ▁Server 186335 ▁جمهور 186329 ▁miejsce 186325 neg 186323 ilang 186314 ▁կարգ 186314 iyet 186311 ▁अपना 186282 ▁ошо 186279 ▁jednoduch 186278 ▁духо 186275 ▁টি 186268 ▁atención 186267 ▁keres 186267 -04 186266 ▁seiner 186265 ▁BL 186255 داد 186254 ခါ 186253 ▁elabor 186249 ▁uning 186246 ▁Servi 186240 uphila 186239 ▁छो 186232 ▁самых 186230 obod 186218 ▁NI 186212 atakse 186209 ▁Luis 186185 ኛው 186163 ▁срещу 186161 ▁지금 186144 ڻو 186124 жит 186121 ▁Maur 186113 ▁խորհրդ 186107 ებენ 186101 XX 186095 დო 186083 ▁Bhí 186083 ján 186081 ▁Vat 186077 īti 186072 령 186062 ời 186049 ීම් 186042 ຶ່ງ 186042 ▁каде 186015 osit 186008 ▁Maa 186008 ضة 186001 ▁مظ 185993 ▁unrhyw 185991 ▁Time 185987 ▁fór 185987 有限公司 185983 ▁парти 185980 રુ 185977 UY 185969 ▁фил 185963 겨 185963 ▁विभिन्न 185958 ▁Verg 185956 ▁shafi 185937 ЙН 185933 ▁vuotta 185932 汽车 185925 ▁Nin 185913 ▁тілі 185906 ▁හො 185901 ▁acara 185890 obus 185887 影響 185886 ▁hib 185879 พิเศษ 185878 stud 185871 ▁paso 185869 usega 185856 ▁поз 185847 цкі 185832 ezwa 185810 ơi 185810 ▁बातम्या 185806 ▁copii 185803 !“ 185798 imaan 185791 ләнә 185791 ▁συμβ 185774 ▁ത്തി 185772 ▁xxx 185770 aming 185764 шыу 185761 ▁עבור 185756 пка 185725 ▁બો 185720 ▁بغ 185687 ▁copil 185662 ▁eno 185658 ▁Patr 185654 ▁cạnh 185649 ▁पढ 185642 ▁бло 185639 ▁शब्द 185638 ጣት 185629 ▁ema 185625 ឹ 185614 ực 185611 ባት 185610 ▁svak 185608 ▁looga 185607 ▁ihn 185596 estro 185593 ▁nz 185587 時に 185572 iaan 185567 ▁ျဖစ္ 185567 어요 185566 ኳ 185558 קא 185547 מס 185542 ▁bibliote 185538 ▁профе 185537 واء 185533 ▁Walla 185521 amana 185512 ▁enemmän 185512 ▁برید 185511 andır 185502 ▁решения 185499 ▁estilo 185485 டும் 185481 niu 185474 ▁administra 185471 我国 185471 ಂದ್ರ 185470 ▁தொட 185469 ▁शिक्षा 185456 ▁seksu 185447 naj 185443 ▁الفر 185438 ▁മി 185438 由于 185433 տի 185431 ლოს 185431 ▁เรื่อง 185423 mund 185421 යෝ 185418 ▁ktorí 185404 opt 185400 研 185385 ▁nossa 185382 ▁урта 185380 ▁зи 185360 फ़ 185353 ▁Agr 185314 ▁क् 185303 ▁zelen 185300 ▁Lot 185294 ▁Ці 185294 ખા 185277 ▁ग्र 185275 ▁mese 185271 ▁ayat 185270 gani 185254 ▁семей 185252 duz 185250 წავ 185244 ვლი 185238 ▁ያስ 185238 ▁눈 185233 ▁Bosh 185230 散 185227 ▁poc 185226 ▁kanthi 185223 そ 185218 ▁принци 185216 价格 185213 ▁ڇڏيو 185212 бля 185205 ങ്ങളെ 185200 ▁minimal 185193 ▁þó 185185 ▁ندارد 185185 ▁mettre 185182 ▁đạt 185181 ▁કરવામાં 185164 ▁پہ 185158 ਬਾ 185153 ทอง 185152 最后 185149 SD 185148 iev 185126 ▁hè 185125 下さい 185123 ▁тог 185115 aning 185112 最後 185105 ▁تلك 185094 ใกล้ 185090 ▁نت 185090 ▁Цен 185086 ▁Xe 185080 רן 185076 ім 185069 ທະ 185063 ▁toki 185042 ▁terbaru 185041 ▁jei 185039 ▁հայտարար 185038 ▁keessaa 185032 ▁flor 185027 ▁ໄປ 185023 ▁mainaig 185018 ุ่ม 185012 ғым 185011 uoti 185003 になって 185002 มีความ 185001 ▁pó 185001 ▁дараа 184997 ▁kwanza 184994 ▁trup 184994 ▁вар 184990 ingon 184979 edit 184970 eji 184953 ילו 184950 ▁State 184937 แก 184925 ▁мира 184925 ▁genyen 184923 ▁Sé 184919 ียบ 184912 記事 184903 득 184894 ப்போ 184878 ▁slap 184869 ▁ube 184860 ĩ 184848 ▁cita 184837 遠 184834 jući 184814 ▁ଥିବା 184806 හන 184794 ▁tinggal 184790 ▁unes 184783 ▁mawalan 184777 ▁mix 184776 ▁finn 184770 ▁Ful 184766 രിച്ച 184763 ግባ 184750 ▁trabal 184749 ▁commen 184741 ▁punkt 184739 ▁لینک 184739 的小 184729 ▁hrvat 184723 ▁Fund 184721 ീയ 184717 ĺ 184711 ▁អាច 184708 ৎ 184706 ältä 184697 în 184691 issi 184686 issance 184684 وره 184677 ▁menor 184676 sión 184657 ▁Sac 184653 يان 184648 ັ 184645 oq 184643 ▁हुनु 184643 ▁təşkil 184640 ექს 184633 ▁naudoj 184625 ▁hatt 184618 ▁Tema 184613 ▁ficar 184612 ▁والع 184611 율 184610 ▁sistemi 184609 ▁رقم 184600 ոստ 184583 ▁कमी 184581 ▁depart 184577 ਾਸ 184573 ▁አም 184570 anding 184568 რების 184565 ▁sır 184552 କ୍ତ 184549 ▁बने 184547 ष्ठ 184534 ▁එහෙම 184533 ▁xizmat 184518 ▁міста 184512 juje 184506 ▁Kateg 184501 ▁συγκ 184495 ▁ede 184490 しま 184476 ▁FA 184468 퍼 184468 hek 184464 leti 184461 ▁совет 184446 普通 184432 ของคุณ 184395 ▁anunci 184393 ାଣ 184387 ▁təmin 184387 itura 184385 ▁tepat 184385 lick 184377 īta 184377 ერთი 184377 Ը 184367 eyay 184357 ellement 184349 Per 184348 ▁tenir 184346 ERT 184339 ▁<3 184337 ▁جاري 184331 ▁вър 184325 തിയ 184321 קת 184314 ▁ազատ 184308 ▁sociales 184298 твор 184292 ▁ثابت 184289 ▁ერთად 184287 ▁haka 184283 ▁raport 184279 供 184279 jons 184277 ▁дово 184276 ▁jihar 184275 ေမ 184272 ்த 184260 ▁арты 184255 ▁Deus 184254 ▁стороны 184222 ▁سما 184218 ▁minggu 184202 ÁL 184197 voll 184192 ▁territorio 184192 пом 184191 eshi 184181 اسب 184167 ▁lagt 184166 rut 184164 ▁Olym 184161 ▁temos 184159 atuan 184153 ▁ezag 184113 ▁Musik 184109 ▁ارز 184104 ▁гадоў 184098 ▁Watch 184089 ▁מספר 184085 ਕਰ 184083 ▁حکم 184079 ▁ახ 184070 ▁VII 184068 ▁Bac 184055 ▁dobrze 184048 ốn 184043 Univers 184033 ▁извест 184032 ills 184025 ુક 184019 ▁اث 184013 ulen 184010 ▁mesto 184001 ложи 183998 څ 183993 ▁euch 183992 шай 183991 ▁EP 183985 ▁испо 183985 ข้าง 183983 ▁ተጠ 183977 ไข 183971 ▁okung 183960 ▁abans 183951 ▁alaa 183942 ọc 183938 blogspot 183937 úla 183915 Art 183904 ▁esku 183902 важа 183898 ▁הכי 183894 ▁butik 183893 ▁комп 183893 正在 183889 ▁polí 183888 :18 183871 ▁medios 183870 ferð 183854 መል 183848 ▁estre 183832 ▁liga 183830 ▁além 183812 ▁करण्या 183811 ▁sepse 183796 ▁хамгийн 183795 nete 183794 ▁prit 183794 ▁bron 183790 ▁agua 183788 үүлж 183783 ▁simples 183768 hä 183753 ▁válto 183753 KL 183751 ագործ 183749 ▁gleich 183742 -01 183737 ▁નહીં 183732 全面 183717 ahy 183715 ▁Pun 183715 ನೆಯ 183711 כון 183697 ▁Rumah 183690 を作 183682 ▁koll 183679 ▁‘‘ 183679 cznie 183667 chio 183661 ▁lex 183658 ถาม 183656 EX 183647 ▁ਗੁਰ 183639 ມະ 183636 adem 183628 entina 183609 ▁ေက် 183605 ▁eig 183602 zini 183585 ▁समा 183583 hada 183571 එ 183562 ▁deras 183540 icin 183535 ቢያ 183534 ▁конкурен 183533 ▁manu 183532 ▁номер 183529 辦 183529 aniy 183525 landır 183522 ବାର 183515 ▁vér 183509 ექ 183497 ▁Կար 183494 ▁80% 183491 ▁انگ 183471 ▁făcut 183470 기에 183460 ▁vídeo 183451 ▁какой 183446 ▁unu 183441 erais 183440 ▁calidad 183439 ▁возможно 183436 ussen 183425 ▁Mask 183419 נא 183416 ▁zamanı 183415 ▁Seri 183414 പ്പോള് 183411 ▁accept 183407 onj 183405 štu 183401 ীন 183371 ▁chịu 183362 ▁isaanii 183358 ことに 183358 ▁gig 183353 してお 183334 ▁δο 183331 越来越 183331 ▁właści 183329 Die 183318 ▁vaat 183316 или 183314 ارہ 183314 ▁අඩ 183303 ගය 183299 ▁kegiatan 183288 ▁дү 183288 ▁لإ 183273 कर् 183271 லில் 183268 ▁۶ 183253 ▁başlay 183247 ▁verg 183247 ▁المؤ 183240 asal 183238 nag 183232 ▁setembre 183232 ▁пои 183230 āv 183226 ▁leder 183226 ▁väldigt 183216 itaria 183203 ▁cerc 183195 ▁हामी 183194 ▁କୋ 183192 ▁НАТО 183187 ▁ഏറ്റവും 183177 ार्थ 183175 λον 183154 ഴി 183145 ▁comple 183145 ▁vard 183138 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182632 ▁desen 182628 ▁ниво 182628 ology 182625 ▁Baru 182620 ▁brut 182618 有关 182618 ▁آواز 182605 ▁круп 182595 ▁Corn 182591 ▁футбо 182591 .08. 182588 ▁යුතු 182584 ighe 182580 ေဆာင္ 182572 ▁yoz 182560 сәк 182551 ▁ստեղծ 182543 akkan 182527 ivad 182526 ▁komma 182512 었습니다 182498 Ки 182495 ୌ 182488 tail 182487 ಸು 182485 illen 182480 ▁פאַר 182480 ▁první 182476 ▁СА 182462 ုန္း 182460 ▁hitz 182451 轻 182443 ▁தெ 182442 ▁દી 182434 asten 182428 ▁တစ္ 182415 ▁mật 182411 سک 182410 開催 182395 િટ 182391 cione 182384 สด 182380 ึง 182371 ▁Trad 182371 床 182362 ▁hores 182360 ▁baya 182351 ▁azi 182350 ອາ 182348 ▁kaca 182347 ▁xử 182337 maktadır 182335 ▁Arr 182325 ▁prag 182317 ▁gewoon 182312 ▁تماس 182306 ▁امر 182304 жай 182299 افة 182296 健 182289 alang 182282 aların 182265 ិក 182249 bund 182243 ▁cade 182240 ▁jú 182232 ▁túl 182215 rech 182195 ▁talent 182190 ▁deleg 182182 理由 182164 ▁കാര്യ 182161 angkat 182149 ▁عورت 182143 ▁Bitcoin 182141 ใช้งาน 182127 ▁تھے۔ 182124 مای 182122 ▁ਸਾਲ 182118 ▁состоя 182115 ▁правила 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181702 できない 181673 ▁nő 181671 СИ 181670 ▁Gur 181670 移 181670 ▁урын 181669 ▁адміністра 181662 ▁Dani 181651 ضح 181643 ▁मध्ये 181641 宿 181636 事情 181634 ούμε 181624 orum 181623 ▁ελλην 181623 ต่าง 181613 帮助 181609 ▁الصح 181608 ಲಾಗ 181607 ائد 181605 เหล 181603 ▁principalmente 181600 ▁2008, 181599 ▁Samp 181598 говор 181597 ▁एस 181574 ▁Ngok 181551 ಾಗಿದೆ 181546 ▁rẻ 181544 ▁Jones 181534 ësisë 181533 ibre 181510 ▁поддер 181496 ▁ود 181496 Bo 181488 ▁aikana 181486 ණු 181483 לעך 181479 ▁Ву 181472 дка 181471 masına 181458 pris 181446 ▁specjal 181442 ▁ngh 181436 জন 181422 ▁tiempu 181418 ▁ngol 181414 нес 181409 ▁dyn 181408 ▁sklad 181403 ▁hehehe 181396 ▁edilməsi 181389 項 181386 ិច 181385 ல்க 181381 ▁ukwenza 181375 ▁lengas 181364 Alba 181359 ნე 181354 ಅ 181352 ▁روان 181352 wend 181347 ▁എസ് 181341 ▁байгуула 181325 KS 181324 ולם 181324 7- 181314 ▁mre 181307 ồi 181299 ▁ew 181288 ▁ನೋ 181286 ▁Tv 181284 ▁prix 181282 elij 181263 ട്ടുണ്ട് 181262 եցին 181260 ▁NU 181259 ▁waliin 181256 ▁porte 181255 交易 181252 õt 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▁данас 180812 မ် 180798 ▁Tx 180792 ▁sää 180792 čev 180791 ▁Kurs 180788 uud 180783 ▁czyli 180782 ▁љу 180776 ▁Интернет 180774 ▁conserv 180771 ▁vận 180770 vens 180762 ▁müəyyən 180761 ക്കിയ 180760 alus 180754 ▁ház 180754 ▁Freonen 180753 ▁cuatro 180731 িকে 180728 ▁1988 180725 релә 180722 ឃ 180715 ▁membawa 180713 ▁Detta 180701 ndaki 180686 ეგ 180685 nn 180678 voje 180677 ▁hardcore 180672 лаған 180671 ▁Tum 180650 ندان 180648 ▁KWA 180620 iline 180615 inink 180614 ▁долго 180614 ▁اصل 180613 क्ति 180589 ▁ukudla 180579 วันที่ 180573 ▁دفاع 180569 ▁ළ 180567 ▁natürlich 180563 ათვის 180546 ▁wao 180538 ▁שנים 180533 ▁tèt 180525 ▁batean 180522 ▁अधिकारी 180520 ▁شن 180508 ıt 180507 락 180506 ▁Sigur 180503 ▁name 180490 ▁school 180484 дит 180481 ▁المتحدة 180480 ▁ಮಹ 180478 ▁шул 180475 acz 180473 ▁Απο 180468 ligini 180462 ลอง 180460 लेले 180459 ▁قسمت 180455 ▁ഇത് 180454 ▁alc 180453 article 180431 áth 180425 alista 180423 ▁shqiptare 180421 смо 180417 ▁terlalu 180414 gers 180408 ›› 180403 вено 180389 ▁бри 180389 ▁ספר 180373 izany 180371 ▁tejto 180370 ▁Mhe 180367 يع 180366 ▁сни 180362 ريم 180355 ▁tujuan 180352 ▁høy 180351 ▁satis 180324 ▁შორის 180321 ▁gemaach 180320 免 180315 inhl 180314 ▁žaid 180304 ▁Tako 180288 ηγ 180285 不知道 180285 ▁Mail 180282 ▁Säit 180278 ▁persoane 180265 ▁такого 180263 ▁ford 180256 ▁електро 180237 vom 180228 կայ 180214 ▁Ats 180208 щення 180205 ▁rää 180200 тров 180189 hop 180188 ▁менее 180188 ▁Rot 180187 рование 180182 KK 180177 ▁kolek 180173 ▁toc 180170 рение 180165 amini 180153 ▁één 180152 ▁ხელ 180152 ▁деньги 180145 ادات 180133 engt 180128 ▁נאר 180121 ▁Marso 180117 ACH 180114 ▁noin 180112 ▁mieux 180110 ▁Nobel 180108 ▁Nang 180105 ▁중국 180097 ДЕ 180088 સો 180085 ▁nélkül 180082 東京 180082 ացած 180081 ibon 180071 ▁ತಿ 180066 ▁πρόσ 180064 ▁פור 180064 ▁vùng 180054 례 180047 ▁обли 180020 ▁revolu 180011 ▁تور 180001 ▁görül 179991 ▁nid 179954 ▁περιο 179951 fyl 179947 ▁ngokuthi 179945 stof 179942 ▁pedagog 179941 кет 179938 edon 179935 ější 179933 復 179932 ፌ 179929 ▁namin 179926 кара 179909 ▁υπό 179908 And 179907 impi 179895 πισ 179886 ▁хорошо 179885 ▁deset 179882 團 179879 报道 179879 мна 179878 ดา 179875 ▁sip 179874 ▁эффектив 179871 lyn 179850 ऱ्या 179844 נטי 179837 ▁вельмі 179837 受到 179832 ▁kto 179828 ▁TAN 179822 ▁ụlọ 179813 ▁รวม 179807 ▁جوړ 179801 ጌ 179791 عداد 179788 を持 179787 ▁dekat 179783 kaw 179776 ▁ಮೇ 179776 До 179771 ▁Civil 179768 ▁tvor 179768 ▁Fest 179747 ▁أيضا 179746 ovanju 179733 emus 179730 ▁Marie 179730 ▁kanya 179728 ▁بهترین 179728 ຄໍາ 179723 anin 179722 ▁авч 179719 ▁tilbake 179712 ▁анали 179698 פס 179695 ▁док 179695 ▁مرکزی 179691 ▁ជាមួយ 179690 突 179690 思い 179689 ▁gemeente 179688 ▁Dek 179678 ТУ 179656 ▁მოგ 179655 ▁디 179645 ▁Unha 179641 ▁KH 179635 াকে 179634 ▁ಹೋ 179627 ▁ראש 179623 ющим 179620 ▁najleps 179618 ogli 179614 ▁દ્વારા 179614 ▁Sí 179612 ▁muchos 179612 perti 179607 ▁fana 179605 ▁процеду 179600 ▁ess 179579 으면 179577 ▁ჩემი 179574 備 179574 ▁berri 179572 ▁dram 179572 ▁אלא 179564 ▁links 179560 ▁procesu 179560 ▁आठ 179558 ▁челове 179552 ▁bic 179550 ▁Kaw 179549 ▁ولایت 179544 ▁només 179542 ▁убе 179539 elés 179528 ▁պահ 179500 ▁йылы 179481 ▁сми 179473 ▁lép 179466 ▁Deutsch 179459 ▁الآ 179453 რები 179451 ▁Pä 179449 கிற 179448 ▁Roc 179447 ▁(14 179439 ▁σχε 179421 ೇಶ್ 179417 нци 179413 ▁Lov 179400 ιε 179396 ogn 179384 ▁Past 179381 ▁телеви 179375 нить 179374 ▁ovaj 179360 ▁Lebens 179356 ához 179345 ▁vezes 179341 ▁כת 179339 ▁antig 179337 ▁οποίο 179335 ▁ឈ 179333 גים 179322 fam 179321 ▁வை 179317 τζ 179313 ▁Ń 179303 anı 179300 ▁lands 179293 ▁niitä 179291 ▁bagus 179289 өм 179283 ▁მიერ 179276 ▁metr 179274 ▁zde 179271 ▁koffie 179266 َّ 179265 勝 179262 ▁naon 179260 ▁gửi 179259 igs 179256 有效 179250 ставля 179248 茶 179243 草 179241 ▁sidste 179238 ▁fier 179235 ▁дола 179227 راق 179217 !? 179199 ▁mane 179193 ▁рецеп 179193 žo 179160 ěr 179157 ībām 179143 ▁Wurkje 179136 право 179130 ▁Texas 179130 保护 179130 牌 179129 ▁Matt 179124 ገን 179122 ▁How 179118 ▁Uku 179118 ▁федера 179117 kau 179108 ▁podpor 179104 ▁Ağ 179098 찰 179096 ▁ໃ 179083 менту 179070 ▁השי 179066 ▁ଶୁ 179066 ▁پاکستانی 179065 ▁তথ্য 179062 ▁Haf 179061 ▁പ്പ 179054 永 179050 00- 179043 ▁ലോക 179043 ▁сдела 179037 szak 179036 ▁januar 179033 ▁качестве 179030 ▁eş 179025 ▁thần 179013 ▁автомоби 179001 рендә 178999 ▁Artik 178993 मन्त्री 178990 lijke 178989 ▁شع 178985 ▁motivo 178982 anana 178977 ▁Nhưng 178977 ▁ପାଇ 178977 リア 178977 эгд 178974 ▁comprar 178969 ▁обја 178963 inėje 178962 ▁opple 178961 ▁qever 178960 ▁ส่วน 178960 状態 178960 ▁znak 178958 ▁drei 178957 ▁وز 178955 ows 178954 ▁ನೆ 178954 ▁स्वा 178951 ▁puno 178942 ivanje 178936 bə 178927 ▁мая 178927 ילים 178919 კის 178915 stro 178912 ▁ngon 178905 udia 178893 רונות 178886 ▁Get 178879 ▁anggota 178877 ▁народа 178875 ▁Lik 178873 ince 178872 ▁dönem 178871 òri 178870 כז 178869 ▁представля 178861 हु 178855 ▁naha 178855 ▁rohkem 178852 ▁ነበር 178851 ▁حدود 178846 ▁තවත් 178836 ▁saking 178832 נם 178826 ▁Lub 178824 കം 178800 bt 178791 esit 178779 čov 178775 ▁ขาย 178760 出了 178760 ▁preci 178758 ▁enter 178751 талі 178746 ▁konflik 178744 ▁нея 178726 លោក 178725 ดิน 178723 ▁fuera 178722 xx 178719 ▁өмнө 178718 خه 178706 ▁Australia 178705 ील 178704 ▁աղ 178688 ▁sabi 178686 男性 178681 ៀន 178677 ▁bino 178675 ▁máte 178674 ▁مط 178667 бна 178666 ▁MI 178664 ▁muita 178662 পি 178657 ▁نیوز 178657 ภาค 178656 延 178649 ▁hez 178635 ثل 178634 ▁μπ 178627 ▁riêng 178620 ▁ceann 178610 ▁Massage 178609 ▁baca 178604 ▁suoi 178602 ಾಕ 178601 ▁informació 178595 ▁dimana 178593 ▁ohun 178587 ▁żeby 178587 vija 178579 ▁жилийн 178577 шло 178571 ▁đáng 178547 ilish 178543 ▁direkte 178542 ▁आरोप 178538 śli 178535 alaki 178526 ▁Ata 178523 ▁тысяч 178518 ▁ব্য 178518 भार 178515 ওয় 178510 ▁лекар 178502 apak 178501 ڌي 178491 ▁설 178487 ▁Ком 178473 cell 178457 ensia 178456 ▁стандар 178455 inaan 178450 ▁Canada 178447 ellos 178438 ▁пример 178437 dhe 178426 ızı 178426 лькі 178421 樂 178419 ผิด 178413 ▁للأ 178403 ▁Universidade 178401 ▁하지만 178398 pros 178396 ▁طريق 178393 ▁бағдарлама 178389 øl 178370 ▁ਬਿ 178367 óirí 178366 mav 178359 etting 178351 ▁према 178350 ▁correspond 178348 ▁eme 178340 错 178339 zir 178338 ▁журнали 178337 ▁obec 178321 ▁арти 178318 ▁Sym 178311 ▁psic 178306 ▁순 178290 hľad 178285 ▁noho 178285 ▁Улар 178274 ▁ums 178273 ▁থ 178273 ▁широ 178271 ▁күні 178264 ▁հանր 178262 ▁আমাদের 178254 ▁Kra 178251 ▁граду 178242 ▁pagi 178235 ▁meille 178230 ▁weekend 178227 ▁خاطر 178205 ▁Gymraeg 178189 ▁تازه 178174 ▁тие 178167 ▁szó 178156 ▁Европ 178149 获 178148 ▁культуры 178147 ▁සිටි 178145 しない 178144 ▁сила 178136 ▁yli 178132 alarni 178124 ▁طب 178114 ▁собой 178113 更加 178107 ▁Jim 178096 ▁nilai 178089 ▁smer 178088 ▁نقش 178086 ▁Katonda 178081 ▁цаг 178081 ▁dogod 178079 ▁maq 178074 рыс 178071 ▁attiec 178068 ▁minangka 178068 ▁راست 178046 忙 178038 ▁Ep 178031 ▁पूर्ण 178030 ▁meld 178024 ▁سام 178015 ▁görün 178014 াধ 178006 ▁এস 177995 번 177987 ugg 177986 inyaka 177983 ▁miner 177980 备 177971 位置 177970 ۔۔ 177962 ▁ຫມ 177962 ▁сві 177958 зии 177952 يين 177951 ▁άν 177949 ▁Segur 177942 ಜೆ 177938 हरुले 177920 манда 177917 ugal 177914 ▁անձ 177913 ▁muze 177908 ▁niti 177908 ▁ରହିଛି 177907 ▁אומ 177903 τήσεις 177888 亲 177884 eldig 177877 خاص 177873 ▁ਅੱ 177864 ▁MEN 177854 ▁Two 177851 ▁покуп 177851 ▁Ор 177850 ▁slab 177843 वाई 177839 ▁страны 177837 ሰው 177835 ▁anv 177834 ▁иза 177830 ▁servizio 177829 ▁νέα 177828 ▁mayoría 177827 ▁један 177825 oft 177808 ▁empat 177807 ▁έναν 177803 ▁isp 177795 ခ်င္ 177790 ▁ddi 177779 ▁помощью 177779 skat 177778 01 177772 ▁River 177772 agb 177771 огра 177770 рос 177770 ▁arbejde 177768 ▁trecut 177768 ▁वर 177764 τύ 177760 ▁নাম 177760 ▁typu 177759 ▁yılında 177752 態 177750 사를 177740 ▁informacija 177739 ▁kancane 177738 ▁alapján 177733 γει 177730 ▁שיש 177718 ▁yam 177716 ▁דבר 177714 ▁стати 177711 ▁เครื่อง 177706 ▁papild 177702 不到 177698 ▁shugaban 177694 ▁weten 177693 örd 177688 กํา 177688 ▁սահման 177675 ▁tulev 177670 ▁June 177661 ▁ନିଜ 177637 ▁realiza 177635 yada 177633 lys 177628 ▁hebt 177628 ▁другой 177605 静 177590 AJ 177587 設 177585 ▁víc 177568 ▁Greg 177558 äch 177556 ▁Invest 177556 ▁foglal 177555 ▁ജോ 177551 ▁sne 177547 อิน 177541 ▁Въ 177541 ėmis 177539 ▁seal 177526 ุ่น 177523 īgas 177521 न्या 177519 ▁kni 177511 ▁yüz 177511 ▁앞 177503 这样的 177499 ▁Ingabe 177492 ▁اہم 177489 ▁reer 177482 osen 177480 ▁столи 177479 лива 177477 继续 177475 ▁CP 177462 žas 177457 ▁॥ 177456 參 177442 óta 177436 olik 177429 ▁tolos 177427 ▁دول 177426 itha 177419 pond 177415 ன்ப 177409 ▁stedet 177402 쪽 177399 ▁ase 177389 ▁Нико 177368 МЕ 177365 ▁ศ 177362 ▁есеп 177359 。。 177357 ▁hran 177337 ▁형 177331 ogan 177324 时代 177318 ▁spé 177313 ▁صنعت 177312 neh 177309 ▁egz 177309 acy 177304 वर् 177297 ופה 177290 ▁пря 177289 nað 177285 สา 177276 agam 177275 ▁ኣ 177270 იც 177265 ▁terb 177257 ▁ذات 177252 ▁assist 177250 ▁danda 177236 ▁nanging 177236 ંચ 177235 ▁števil 177231 ▁pasta 177227 ▁сада 177224 კვ 177215 ▁azonban 177214 희 177201 托 177197 ▁precisa 177194 ▁comunicación 177189 ▁υπάρχει 177184 utum 177169 ICE 177163 ໃຫຍ່ 177152 ૮ 177145 job 177139 ჯერ 177139 คาร 177134 ▁सरकारले 177131 areo 177130 เดือน 177116 ▁එහි 177114 abanga 177108 ▁irány 177107 ▁TOP 177098 ▁เช 177097 ▁Пры 177090 دید 177089 ▁imb 177083 чів 177079 ડો 177064 ▁Mate 177060 lade 177046 ▁proposta 177038 ▁सुन 177037 ▁siihen 177035 ▁договор 177034 eir 177028 ▁strategi 177027 ▁пя 177022 вості 177021 ▁fantastisk 177021 ουλ 177012 牛 177009 ▁тај 177008 ЛО 176998 ▁asupra 176998 ومة 176995 ▁Thủ 176993 ▁подат 176986 lid 176965 ▁ně 176950 ▁majú 176947 ▁Blue 176932 ▁Mwen 176927 ▁ಕಾರ್ಯ 176926 ▁Alam 176923 ▁उन 176923 āti 176919 ित् 176912 ▁1.5 176899 ontan 176894 ▁своего 176891 θο 176890 ▁tink 176886 nr 176881 žil 176879 ▁جمهوری 176876 elsk 176875 ▁작 176875 ▁لازم 176874 ējā 176872 ▁seseorang 176862 ▁સમાચાર 176847 ▁azért 176846 ГО 176822 ▁выда 176822 ▁SER 176820 ▁128 176799 ▁вне 176798 ▁kilo 176793 ▁гол 176787 ▁لار 176779 정보 176775 angal 176774 inimo 176768 ▁samee 176763 ▁않는 176763 හල 176748 ▁conocer 176731 ▁ձ 176727 ▁oferta 176725 ▁orig 176724 quez 176717 สู่ 176712 ▁ҙа 176712 ▁таа 176703 ستر 176700 ጀመ 176698 ▁شركة 176694 设备 176694 ▁mjesto 176693 ▁ዓመት 176691 ισμού 176680 änä 176666 ▁Ketua 176661 ಟರ್ 176660 ▁considera 176658 ▁igény 176653 agua 176651 関 176639 ▁budú 176632 ▁gala 176630 ▁결과 176629 ▁Эр 176613 雪 176606 ▁‹‹ 176604 ▁lood 176590 мпа 176577 ▁общи 176562 くらい 176561 ើយ 176553 -03 176550 тельные 176541 ້ອຍ 176530 shop 176526 ርስ 176524 ▁Bilder 176524 ▁підтри 176524 ளர் 176520 ▁भाषा 176517 ▁нем 176516 ¤ 176510 裝 176501 ▁colabor 176499 ▁jste 176494 طار 176493 ▁kinderen 176491 ▁կար 176488 ▁Bisa 176480 ▁xuống 176478 ▁സഹ 176477 ▁όπου 176475 λία 176474 СА 176464 想要 176458 kuu 176456 đa 176454 ▁Pit 176441 ој 176440 ▁مخالف 176430 оны 176429 ▁gradu 176423 ▁właśnie 176420 ▁עוב 176418 ▁call 176415 ▁Respublikası 176413 ▁സൂ 176413 มีการ 176409 ▁Më 176406 ▁Đức 176405 ক্ত 176392 ▁tis 176370 łych 176360 ▁следует 176360 ▁фай 176336 aliya 176328 ītā 176327 ▁Norm 176327 ूर् 176318 chta 176309 ▁fhe 176303 ▁ರಾ 176298 ▁ጎ 176297 /09 176289 ▁tubig 176285 ▁sjá 176283 ําร 176281 ақ 176275 ูล 176273 engar 176261 ...? 176259 ▁intim 176255 雷 176255 ▁grado 176253 ▁технология 176253 ▁СП 176251 ▁Tec 176249 ▁perkara 176243 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▁gesti 167926 ▁സംഘ 167915 ▁jem 167891 ပြဲ 167881 ▁реак 167880 ▁ngủ 167879 ượng 167873 ਦਾਰ 167872 适 167862 ▁cenu 167853 ▁kí 167831 ▁permett 167831 ▁deis 167829 ▁świat 167825 ▁batho 167822 ኃ 167818 ▁бә 167815 ▁änd 167802 ▁uygun 167801 ▁îi 167799 ▁वाढ 167799 术 167798 lių 167794 ▁дове 167789 ▁Ibu 167778 ▁piir 167772 ▁cip 167762 的话 167762 ▁services 167761 ▁कह 167756 ▁confirm 167753 ▁Aman 167752 କ୍ତି 167742 ▁Ня 167740 ▁doel 167737 ▁doi 167734 rias 167732 ▁សុ 167731 ▁dtí 167711 քեր 167709 ▁купить 167705 ▁ေမ 167701 āts 167699 пет 167699 ▁ចំណ 167698 ▁Obat 167693 hard 167691 ▁пун 167690 中國 167687 ψει 167676 ▁descri 167675 烈 167666 ▁усе 167654 iseer 167653 ทั้งหมด 167650 τεύ 167649 ▁ເກມ 167649 ▁sanat 167647 fot 167641 avaju 167637 ▁הוי 167634 änger 167632 čilo 167630 erz 167628 ▁2005. 167619 ▁vanaf 167611 ▁prep 167605 ▁niður 167598 תם 167597 ▁abal 167581 举 167581 ▁род 167578 ▁школа 167573 շտ 167572 ▁dali 167570 ▁தோ 167569 ▁Srednjobosanski 167566 น่า 167562 ▁internation 167540 egal 167538 ▁хийх 167532 ▁итте 167530 ▁šķ 167529 ▁veloc 167521 tena 167520 ▁dieta 167516 гии 167509 Ð 167507 ndir 167504 ریف 167504 ledning 167475 เสียง 167470 ▁Бөтә 167469 ias 167467 ାରା 167458 ller 167450 ▁família 167442 ▁شیئر 167441 ▁ජන 167437 ▁vitin 167435 tų 167431 รุ่น 167430 ліза 167429 ▁وقتی 167426 ▁第 167426 ρει 167422 ▁เด 167422 jaz 167418 єм 167412 ▁ማን 167409 ▁implic 167405 ▁וויי 167404 ▁venir 167396 ог 167388 आई 167385 mada 167372 larımız 167370 vall 167368 ▁vállal 167365 VE 167358 ▁ජී 167357 મ્ 167352 ▁Datuk 167352 zuk 167349 ▁vpra 167342 ▁والإ 167338 ▁abaz 167336 อะ 167333 ▁partea 167325 ▁tarjo 167324 ▁quelle 167311 ▁bezala 167308 ▁Євро 167297 ▁həmin 167292 ▁odst 167292 ▁ngabe 167285 സ്ത 167283 ▁ಸ್ಟ 167277 無料 167268 ▁duzu 167265 ರುತ್ತ 167262 ▁Kaip 167254 τητας 167249 ▁kter 167248 кове 167234 預 167233 ▁ລາຍການວິທະຍຸ 167231 ▁ರಾಜ 167228 adeed 167222 ▁bik 167222 ▁vielen 167222 ▁trav 167220 φέ 167216 ▁artean 167212 ▁Tän 167205 альный 167204 ລາຍ 167193 ▁gana 167192 ூர் 167190 အသ 167189 top 167187 ੰਦ 167187 ▁היו 167187 ▁kny 167185 ▁profit 167179 ▁ପି 167170 தும் 167166 kih 167153 บอล 167150 ଧ୍ୟ 167144 ▁puedes 167140 ▁njihov 167138 چی 167137 cijama 167135 ▁interessante 167124 ▁рішення 167122 ▁ሐ 167120 elä 167110 хар 167108 ▁sık 167101 楼 167100 ങ്ങളില് 167083 ▁Victor 167080 wers 167068 ezu 167063 нта 167058 ገለ 167051 ▁ένας 167050 替 167048 ▁ຮັບ 167046 iesz 167045 cké 167037 ▁territori 167028 ▁быстро 167022 ിക്കും 167020 зон 167016 ▁состав 167011 ▁Ces 167009 ▁común 167007 ▁peale 166997 ▁ભારત 166997 ▁kelas 166996 rës 166995 jave 166992 ▁opt 166989 ▁rog 166987 ωσε 166981 گل 166980 ▁münasibət 166980 visning 166979 ▁gadu 166976 检 166976 ▁جماعت 166975 ▁hü 166966 лож 166961 kke 166954 ほ 166949 ▁ห้อง 166948 ▁თუმცა 166938 租 166934 ▁இட 166922 ▁용 166922 ▁Bali 166918 agay 166909 ▁কথা 166907 صة 166904 ▁മേ 166904 сце 166899 քան 166896 ከተ 166896 にある 166894 ▁yeo 166885 ▁måtte 166882 ▁perioada 166878 みたい 166878 ▁Sør 166877 ▁kapital 166861 ▁желе 166853 тыг 166851 ▁Yap 166849 ▁bantu 166849 ▁życia 166849 ▁ದೇ 166836 ▁Coll 166825 ിയെ 166819 ▁сю 166814 ▁mogli 166802 ▁vār 166797 ▁protože 166794 ▁фән 166794 alip 166790 qonda 166775 ▁ادار 166766 ủ 166751 ▁Aya 166750 ▁Bers 166741 ▁pamamagitan 166740 ords 166733 ෞ 166724 ▁waw 166722 ▁yaşam 166721 entzia 166717 ▁posti 166715 ▁Ова 166706 ▁Bara 166705 க்கள் 166704 fə 166696 ಾಜ 166693 ▁história 166692 ▁aplikasi 166686 шего 166683 ▁Durante 166682 سٹ 166673 මන 166671 йд 166656 რებ 166655 èa 166653 اڻ 166650 專 166646 чког 166644 ▁computer 166644 ျပဳ 166642 ▁ahl 166634 ▁خصوصی 166603 ▁poni 166601 ▁انہیں 166598 相信 166598 ▁halda 166597 ▁مک 166589 ▁Ked 166585 ▁նույն 166585 ▁práv 166582 ানের 166579 ▁фору 166579 ▁لکھ 166578 ▁реда 166577 ▁Carr 166572 idé 166566 налі 166566 ▁भू 166566 ▁ואי 166559 imia 166554 ンス 166552 ▁Ms 166542 ▁enkel 166538 ayat 166525 ▁ବେ 166522 ▁Cass 166514 ▁noms 166511 ▁Shop 166507 ▁эм 166503 ▁coup 166493 ▁պատասխան 166488 ▁заста 166486 illing 166482 ▁Milan 166464 រស 166462 ▁첫 166455 ▁mengg 166448 িয়ার 166444 ▁através 166438 โน 166436 ▁võimalik 166435 ክት 166430 ▁കഴിഞ്ഞ 166416 确 166407 ਪਰ 166401 ▁Kara 166395 ▁koz 166390 ▁dzie 166387 該 166387 ▁ဘာ 166386 ▁բնակ 166383 ▁rekord 166382 ética 166380 ▁2018: 166377 ▁njerëz 166376 ▁trenger 166360 вани 166357 žne 166333 ▁əlaq 166330 ▁кей 166324 ಬ್ 166314 模式 166310 дной 166298 ▁tillegg 166298 ▁चाल 166296 ▁URL 166281 简 166279 ▁waarop 166276 ▁අත් 166273 ೂರ್ 166260 kia 166259 ▁ron 166257 ▁Õ 166255 ആ 166251 ▁něco 166250 ▁mostrar 166249 ရှိ 166243 удын 166242 ▁ইসলাম 166239 ▁Ima 166238 antung 166234 ▁האח 166213 ▁힘 166212 ▁Promo 166204 ▁հայտն 166204 קור 166202 ▁welche 166197 ▁əvvəl 166190 andal 166187 test 166187 ▁nees 166183 በሩ 166181 ▁Poker 166179 ▁sebelumnya 166171 ▁mno 166166 andar 166157 ദേശ 166157 மும் 166153 ▁Intern 166151 ▁הנו 166148 ▁Сту 166147 ▁보기 166145 ▁درد 166135 週 166132 ັ່ງ 166111 ▁រដ្ឋ 166110 ▁ಸರ್ಕಾರ 166107 ိုင္း 166106 ▁Card 166099 ກໍາ 166098 ▁დაი 166096 ▁təqdim 166093 шын 166077 ampak 166070 ▁ເມືອງ 166070 ▁etap 166069 ▁uair 166068 卻 166066 adress 166064 ▁اقدام 166060 ▁subs 166055 রণ 166054 ▁خانواده 166047 agement 166042 тергә 166036 ални 166034 ▁তিনি 166034 ാത്ത 166029 ingga 166021 ▁Qo 166020 ▁bahay 166013 ▁imen 166013 čenje 166009 ▁UV 166004 ▁vienas 166002 ▁famp 166000 ▁obsah 166000 ▁ਵੱ 165995 ିବେ 165988 ▁perder 165987 чним 165985 هين 165962 ссия 165959 ▁جهانی 165959 ▁adı 165952 ▁datu 165942 ▁sukses 165940 ▁мәселе 165934 ▁Тех 165932 ▁конструк 165932 τία 165931 িট 165931 stelling 165929 ▁konkurs 165929 āls 165927 ▁അഭിപ്രായ 165926 ftet 165922 нням 165922 кры 165914 тили 165897 ▁مقاله 165897 ▁καν 165896 pec 165895 rava 165888 ρωση 165888 େଶ 165886 ▁ሆነ 165886 ohon 165883 くる 165881 ▁тағы 165878 ▁box 165872 ▁අම 165872 ▁څو 165870 ▁saúde 165867 сну 165861 MO 165859 ▁описа 165857 ▁permanent 165850 huis 165846 र्ट 165842 ասն 165830 ▁νό 165830 ▁kommunik 165821 ▁attraverso 165819 onse 165818 ▁چشم 165816 ▁вечер 165813 ക്കള 165809 ▁Kata 165803 ផល 165802 ▁Astr 165799 ▁Produc 165797 ▁eraill 165785 хий 165784 ಿಕೊಳ್ಳ 165782 ▁страхо 165779 ▁pili 165773 tex 165771 ▁mají 165764 フィ 165763 ▁Elektron 165753 ▁llegar 165753 ▁ನಾನು 165745 नाथ 165728 ▁شورای 165720 ▁þessu 165717 ټه 165710 ▁අනු 165691 alarda 165689 ▁soms 165686 ▁SAM 165683 ▁профи 165680 ▁සමග 165680 ောင်း 165679 ▁així 165675 ၉ 165674 ▁cila 165656 ▁najve 165655 зывает 165654 ▁siebie 165653 ▁ուղղ 165647 ▁մասնակց 165642 ନ୍ୟ 165636 ▁musical 165635 ▁njengoba 165634 ▁Siya 165625 ▁ПРО 165620 ေျပာ 165612 हरा 165608 ▁પડ 165601 ▁Ona 165600 ▁kih 165593 ▁drugim 165592 ▁artisti 165589 하시 165583 ▁علیه 165577 ρίζ 165575 ▁Кү 165568 ▁кг 165562 ازی 165548 ▁nuova 165544 ▁файҙа 165544 เดินทาง 165543 ▁культур 165539 ▁Để 165538 行政 165527 તું 165520 ▁бүр 165519 กลับ 165517 ដល់ 165512 ▁ასევე 165508 ▁megsz 165507 ▁Meer 165505 ▁adh 165503 jeća 165502 ▁Blo 165500 ្រី 165495 ▁Recht 165490 իստ 165488 ▁فقد 165481 ▁آء 165472 ándose 165467 ▁kolay 165467 -06 165464 ▁Nú 165457 ▁basis 165456 ▁beslut 165454 ysti 165451 ▁பதிவு 165448 ▁Semalt 165447 ▁effet 165442 لاق 165438 ▁투 165438 čeno 165435 قيق 165435 ▁Institu 165434 ▁kinn 165430 อัน 165425 inia 165422 ▁etapa 165417 lun 165409 ▁россий 165402 妻 165400 ---- 165399 ▁pamat 165399 ▁QUE 165398 intza 165388 ▁meest 165385 យក 165376 /2015 165374 itek 165374 ▁گا۔ 165371 ▁pt 165370 ▁ընթացքում 165363 ▁dejting 165362 ▁يجب 165362 ▁Ақ 165360 ▁Euroopa 165353 ▁جيئن 165352 διά 165339 ahla 165328 ▁රෝ 165326 ▁МА 165319 ▁yangi 165316 혜 165312 გრძ 165307 ▁선택 165296 ▁Ibrahim 165294 ▁Nokia 165293 kki 165287 ଦା 165285 ▁тога 165284 ▁oka 165282 bhe 165281 ▁କରିଥିଲେ 165280 ▁gồm 165275 ▁Aquest 165265 ération 165258 ▁길 165257 ▁מלא 165254 ▁masse 165252 ▁taf 165246 ്മ 165239 ▁भयो 165238 ▁ನಿರ್ 165228 qul 165222 ▁기업 165221 ▁Eins 165219 ▁প্রকাশ 165208 ▁uph 165189 ▁ambi 165186 යු 165182 ിക്കുക 165178 ▁Aan 165173 ▁τόσο 165173 ▁küll 165172 જન 165170 这里 165168 ?... 165153 கி 165152 ▁Нау 165151 رير 165149 ▁pubblic 165149 ▁pune 165149 әне 165148 atë 165141 ▁२०१ 165138 ▁Wol 165134 ▁বড় 165128 abit 165124 ▁kadın 165122 ▁легко 165122 ▁vlas 165121 ಾಹ 165120 ▁패 165117 ▁jord 165116 ▁Autom 165112 ▁Dünya 165110 ▁pö 165104 ▁trabay 165103 पन 165100 ▁የስ 165099 搭 165094 ▁සිංහල 165092 ▁치 165077 tijd 165054 ▁Kond 165049 ▁take 165049 ▁وژل 165041 alaw 165036 ▁وړاندې 165036 ▁nouveau 165033 ▁bác 165024 ▁Arb 165023 ieť 165020 行为 165016 ▁Eff 165013 >> 165009 ▁сказа 165006 倍 165002 ▁ಘ 164994 ▁Роз 164993 ▁Sama 164989 ▁අනුව 164985 ارد 164981 rire 164979 јот 164977 ikker 164967 ▁어떤 164963 åg 164962 ▁ਜਿਸ 164962 ▁bó 164958 াগ 164954 ▁شمال 164952 ▁krimin 164945 ော် 164943 ▁lång 164940 ▁willen 164933 ▁тар 164931 όγ 164926 ಿದರು 164921 ▁ск 164919 ▁Pär 164917 ▁задово 164917 ႊ 164910 ▁Ֆ 164885 ▁slot 164881 ▁aján 164875 ▁зара 164872 ▁टा 164869 ▁Katika 164864 хме 164862 ▁ваҡыт 164860 媒体 164857 родно 164850 ულია 164850 pta 164840 ▁mr 164840 ▁கரு 164839 brug 164838 ▁tong 164832 ▁ға 164831 публі 164830 ▁aige 164830 ▁maika 164822 ▁údajov 164812 ্রী 164803 ਜ਼ਾ 164798 ▁квали 164794 ▁развитие 164794 ▁പൂ 164789 PT 164787 ▁mt 164785 軍 164782 ▁pai 164781 jani 164776 ▁lao 164767 ▁rigtig 164759 ▁너무 164754 ଥି 164743 ▁повер 164743 entral 164739 ▁arrang 164739 ▁batzuk 164738 politik 164727 ▁קט 164724 ▁ல 164721 ▁करे 164717 िये 164715 ▁ಮೊ 164710 ૬ 164705 感觉 164704 ▁바로 164702 ▁ДО 164694 ▁altro 164685 ▁Yen 164684 ▁verkligen 164674 ▁Cyn 164673 零 164662 க்கா 164651 ▁Fragen 164641 ▁futur 164627 ţe 164621 ▁Hrvatska 164621 ▁wym 164620 ▁gobern 164619 ▁לקבל 164606 icie 164603 ლოდ 164602 ▁grond 164602 ▁Qual 164585 ▁ਹੋਏ 164582 善 164580 ▁kreativ 164575 ▁tsara 164568 ▁організа 164566 ▁szé 164556 ობას 164545 ▁kër 164544 ▁Nom 164540 itty 164533 ტომ 164533 ▁وکړي 164529 ▁ods 164526 uster 164522 графі 164509 vak 164500 യില്ല 164497 ▁qyt 164492 ยอด 164480 ▁initi 164467 ▁pojed 164464 γράφ 164453 ▁saba 164452 ▁piek 164443 ሆነ 164435 ▁suport 164434 恩 164426 ▁ries 164422 τρέ 164421 ▁хэ 164418 ▁Julian 164410 wią 164405 ▁Inggris 164399 ▁افزار 164399 ર્સ 164396 ▁semin 164396 ющего 164395 ▁сайн 164395 oris 164388 ítani 164383 ▁olar 164373 ▁мин 164370 ▁nasi 164360 إن 164359 රිය 164352 ▁ڪراچي 164334 ▁navn 164326 ոյ 164324 ▁pendant 164319 ▁تحریک 164313 ีก 164312 ▁bast 164312 ▁kutoa 164308 ▁употре 164306 İR 164302 ▁لفظ 164292 ▁Ft 164290 ▁അപ 164284 ▁pagkawala 164283 ▁आपके 164282 buat 164275 ▁المر 164271 ▁चाहिए 164271 ેર 164266 ▁Lug 164255 たち 164252 有的 164252 ogel 164248 雄 164242 ▁mwyn 164241 ▁Barat 164238 のも 164232 ▁91 164231 ▁kamb 164225 ellinen 164220 сок 164217 ▁Nai 164211 ▁Sə 164206 同じ 164200 층 164194 ▁Ingen 164191 ▁ሆኖ 164183 łos 164181 ▁Dog 164181 шылар 164179 ▁llog 164169 ▁වර් 164169 ▁Πρό 164162 ▁Gö 164160 ▁zosta 164156 ▁pocz 164152 ຸ່ມ 164149 밀 164142 ▁Осо 164133 ▁kuwi 164125 ▁kø 164121 מית 164120 mier 164119 ėd 164117 ▁ദു 164113 ▁Tib 164108 ▁पुर 164105 ▁fronte 164103 หมด 164100 onana 164098 ljivo 164090 ▁۷ 164089 ▁سرا 164085 ▁صلاح 164074 ▁شيء 164069 antara 164045 нията 164040 ▁four 164040 ▁પરિ 164038 ադարձ 164035 ▁Leb 164034 лла 164032 ▁liz 164025 resa 164022 ▁wohl 164022 ▁yah 164021 ▁полови 164021 рії 164018 ▁Ahmad 164012 дель 164009 ▁longo 164009 ▁кредит 164009 ▁época 164002 ▁вирі 163999 клада 163996 ▁κρα 163991 èus 163988 ▁harmon 163987 ▁██ 163987 때 163987 מער 163986 课 163986 仍 163985 ▁iet 163981 ▁lær 163975 들에게 163969 ૭ 163968 ್ರೀ 163968 ▁יח 163966 App 163956 纪 163953 ▁Мин 163945 ▁informat 163943 ULT 163935 医院 163931 实际 163931 ▁Numm 163930 ▁dz 163918 ▁berbeda 163906 ▁Амери 163902 сност 163891 ▁kësaj 163877 ▁wè 163876 ▁uusi 163871 åk 163870 ▁کلی 163867 ▁кол 163864 ▁costa 163863 esin 163860 лыу 163860 ▁많 163859 ▁խնդիր 163855 ▁qualité 163853 ▁মান 163851 АТ 163849 ▁الناس 163842 ▁robi 163837 ▁nyonso 163836 ▁vinter 163835 amiz 163829 ▁Prem 163820 что 163813 ▁letzten 163793 ▁ваҡытта 163787 ▁நன்றி 163786 mani 163783 ▁פל 163782 ▁የነ 163770 ▁Arch 163761 ▁bzw 163761 ಷ್ 163753 ▁ум 163751 患者 163741 ▁Mt 163736 рит 163727 ▁turun 163715 ālās 163712 ▁অব 163706 ▁Health 163704 ▁servizi 163700 र्ड 163699 ▁Wag 163697 istri 163696 ilet 163693 ▁oop 163693 监 163693 族 163681 ▁Mitt 163680 럼 163675 bog 163670 ▁polisi 163667 ▁veren 163666 ismus 163658 ▁использовать 163656 ▁chắc 163652 apho 163651 ▁Gae 163650 oped 163644 ▁امور 163643 ▁فوق 163642 കര് 163628 ▁තම 163628 ität 163621 ▁quân 163621 ▁Mic 163616 ▁hehe 163615 ▁historie 163615 性的 163612 ▁которое 163603 ΤΕ 163596 пле 163596 ▁procur 163590 핑 163590 exp 163582 ▁সা 163579 тики 163577 ▁преду 163571 भएको 163570 ▁kahle 163569 ▁достав 163565 ▁lieu 163559 iect 163554 ▁территории 163548 ▁zao 163545 ▁Spill 163537 ▁Ih 163533 ▁ուղ 163529 жава 163528 ▁sujet 163528 шать 163527 ▁svih 163527 ▁၃ 163524 ▁nejak 163519 ▁срок 163516 ▁membres 163515 ▁təhsil 163512 ▁יאר 163507 ▁naw 163506 ▁gratuit 163505 ▁መንገድ 163500 ivamente 163499 니까 163497 ▁obser 163486 자의 163485 ▁specif 163479 ▁elementos 163478 lerden 163470 ▁यस्तो 163466 rann 163457 icama 163455 ▁wathi 163453 ▁naslov 163452 ▁mutat 163432 ▁Rat 163422 แล 163418 ภูมิ 163408 сить 163404 ▁فایل 163402 ▁noite 163401 ▁കണ്ണ 163399 ებულია 163393 ▁sker 163392 ▁prip 163391 仕 163387 valo 163386 ▁750 163375 ▁האב 163360 ▁stuk 163355 ▁ukum 163353 fod 163333 ▁sifat 163332 зит 163327 ▁ေန 163327 ▁الدين 163323 ▁1984 163321 ▁fakult 163309 ▁Teng 163301 ▁محمود 163301 ថ្មី 163297 ▁salon 163290 istö 163289 house 163286 ▁kòm 163283 ▁mör 163282 ▁حوزه 163282 າຊ 163271 ▁paral 163271 γά 163260 ▁personne 163254 ▒ 163249 ▁Cym 163243 ▁Rand 163238 ▁temel 163234 ▁خبری 163228 からの 163223 ingt 163216 ▁新 163216 тивни 163215 אב 163215 ▁tokom 163212 ārt 163206 ▁했다 163205 ▁świad 163195 ▁elas 163187 ▁Що 163187 ▁exact 163185 ôr 163178 ▁ľudí 163174 шку 163162 lot 163161 rā 163154 ▁ਕਿਹਾ 163150 真正 163139 ▁muncul 163138 ▁vseh 163137 ▁ianao 163133 ▁comentari 163130 ▁Avropa 163125 ▁жоқ 163124 lagt 163121 ▁Peny 163118 集团 163117 ▁rau 163112 čios 163109 ▁अधिकार 163109 ▁finanzi 163107 ▁leith 163104 ▁hitt 163102 ▁három 163102 વર 163098 ▁प्रतिशत 163096 ាទ 163094 УУ 163092 ▁nád 163090 ▁ръ 163090 ▁នាក់ 163089 ▁taki 163086 ▁равно 163086 ▁гэтым 163085 իան 163077 ▁পুলিশ 163072 ▁Phil 163068 wol 163065 ▁propria 163064 ▁sya 163063 ▁пыта 163058 ▁phủ 163053 ▁jouw 163050 ▁Daily 163047 ▁беларускай 163047 κου 163038 اور 163024 ▁siudad 163022 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▁Energ 159285 ▁verke 159285 ynu 159284 ကား 159276 say 159274 ลาย 159273 лев 159269 ▁લો 159268 弱 159259 ▁Böl 159258 ▁zusammen 159257 ▁ζωή 159256 ሠ 159253 мана 159252 ▁Isl 159250 ера 159230 ▁нив 159229 ▁--- 159222 ႀက 159217 Azərbaycan 159215 jii 159212 ▁folyamat 159204 ▁Share 159203 ▁କରନ୍ତୁ 159198 ▁דע 159196 ▁اصلاح 159193 yto 159190 സ്സ 159185 ▁Publik 159179 ▁alem 159178 ulación 159176 ossen 159173 ▁tải 159171 ніх 159170 вој 159169 ▁hôm 159164 ▁olmaq 159152 ▁maison 159150 ▁kig 159149 ▁dific 159140 lọ 159136 ▁которого 159128 ▁ဇ 159126 ▁орны 159125 ▁খবর 159124 ▁račun 159115 ykl 159112 ▁enti 159105 ▁mús 159089 កម្ពុជា 159087 ▁ਰਿਹਾ 159087 ▁abin 159082 alif 159079 onak 159078 лит 159077 ศาสตร์ 159073 ▁bune 159062 ▁ହେଲେ 159059 ▁яна 159058 ▁Laj 159056 ▁њихо 159052 ▁dəst 159046 ▁IND 159035 ▁আব 159035 ▁عرض 159029 ▁ജീവിത 159026 券 159024 ▁IG 159016 ▁collabor 159016 ▁ainult 159015 ▁Ministr 159010 כנ 159008 ▁okres 159006 ▁zara 159005 ▁पूरा 159000 ▁plass 158990 iram 158989 ባል 158982 hala 158981 ინო 158981 ▁ngendlela 158974 ▁Jehovah 158947 ▁Games 158940 atuko 158936 ▁оче 158933 ▁gwneud 158920 ▁રાજ 158920 ▁व्या 158919 вог 158918 ▁sain 158912 тет 158888 04 158887 ▁եթե 158885 ▁rais 158884 рить 158882 ▁Cour 158877 ▁templ 158874 ▁هیواد 158874 ▁editor 158867 ▁abo 158861 indre 158860 ▁חבר 158859 ▁setmana 158856 ▁מוצ 158855 ▁IUCN 158851 ▁bò 158851 ▁kalo 158844 ▁années 158838 ▁wied 158834 ÁS 158831 ▁yönet 158829 anar 158813 ▁этим 158810 ▁үйлдвэр 158798 ваа 158794 പ്പോ 158787 ▁ვარ 158784 laşdır 158783 вної 158782 ibel 158779 りに 158778 ▁टे 158777 ▁вся 158774 ▁bê 158767 ▁twor 158759 ▁Guerra 158752 ▁оба 158749 ▁zama 158743 itam 158736 ριά 158734 ▁কিছু 158733 ▁demande 158732 ếp 158729 ostu 158727 лежа 158727 WS 158725 ləşdir 158718 ▁கொள்ள 158715 いたします 158714 ▁läng 158703 રૂ 158700 ▁Material 158697 ▁amar 158695 ▁Demokrat 158691 已經 158688 nul 158687 zig 158685 ▁машина 158681 ▁کولو 158679 ▁törvény 158676 ▁դր 158671 ्टी 158670 她的 158670 ▁Bell 158669 故事 158667 ▁Ծ 158665 weet 158661 ector 158659 ▁Occitanica 158655 дегі 158653 vende 158645 ▁резер 158643 qan 158639 ▁комплекс 158637 дії 158635 系列 158632 ▁parce 158627 ساس 158621 ▁møte 158621 ▁gjen 158619 ហើយ 158615 ▁ዜና 158612 ▁nešto 158605 철 158602 quí 158599 ▁өг 158595 かに 158595 佛 158595 ▁okanye 158583 ▁três 158577 ▁สี 158577 ថ្ងៃទី 158575 ▁архи 158574 ▁lief 158573 ▁chết 158565 ading 158564 ▁არც 158557 のように 158555 ▁образования 158541 ▁gabe 158538 ксе 158534 ເພ 158526 -08 158525 kais 158521 ▁kord 158520 ▁eadar 158517 ▁سندن 158513 šem 158511 ▁rendelkez 158505 ▁ເທດ 158502 ▁Ned 158500 enici 158495 ▁sott 158490 ികള 158489 ))) 158482 ivet 158478 ▁akad 158471 ▁στρα 158467 رید 158466 ▁diret 158464 ▁БА 158457 Mo 158456 вје 158450 ▁panawen 158437 वून 158428 ದ್ಯ 158426 ▁padre 158414 ▁избе 158411 цця 158409 taz 158408 ▁Hvor 158407 ición 158404 ▁Kunst 158397 ▁laptop 158393 ▁Fj 158392 हत 158388 punk 158387 ▁трудно 158385 ▁പോക 158382 ▁შეიძლება 158380 ▁peł 158376 kz 158370 ▁βασ 158370 ▁minutes 158369 رج 158365 ▁päev 158342 ▁വില 158336 ▁vardır 158310 ▁अग 158307 entru 158296 ških 158295 ▁skon 158289 ▁часо 158288 ▁LAN 158274 organis 158268 ▁fans 158262 ▁ली 158260 ▁посла 158251 樣 158247 леш 158244 ▁bie 158241 ilmente 158240 ▁tranqu 158225 ▁ເກ 158220 頃 158214 ▁beynəlxalq 158206 ậu 158203 ▁aldri 158201 ▁presso 158200 ▁verso 158194 ▁BN 158190 ▁وغ 158187 ▁Guy 158185 ហា 158184 ▁позволя 158176 ▁teatr 158175 ▁Uganda 158169 atiounen 158168 ▁ніж 158165 ▁niños 158162 ▁точки 158159 ▁Polisi 158158 enni 158143 ▁Terra 158138 ▁गरेर 158134 ▁sociais 158130 ▁sjál 158129 ▁სულ 158126 ▁Cena 158122 ▁bước 158114 fär 158112 ▁ខ្មែរ 158108 vision 158102 arlo 158101 ▁Golf 158100 ▁ਗਏ 158100 ørende 158089 ▁권 158087 ,. 158078 ▁look 158078 ▁trim 158076 manın 158075 ▁нико 158067 рож 158066 гно 158063 ▁Well 158061 ▁района 158058 ສະຫນ 158040 ▁colos 158040 प्त 158039 ▁Từ 158039 ▁livre 158038 ്യാ 158036 éiss 158028 ଞ୍ଜ 158022 ▁đọc 158016 ▁кара 158016 চার 158011 ▁abandon 158007 ριν 158004 ▁algún 157999 INT 157996 ▁holder 157996 ▁симво 157989 ▁ගත 157987 ▁invece 157985 ▁namen 157985 esinde 157983 atoare 157982 endet 157982 开展 157982 ▁лв 157981 ▁جام 157980 ▁laban 157977 ▁Hold 157975 maks 157962 ▁hacia 157960 ელია 157958 ▁मार् 157954 ▁trách 157944 操作 157942 ▁använder 157935 YS 157934 ▁tutkim 157932 ▁ਹੋਈ 157923 ကျ 157916 وص 157915 ▁жауап 157914 atiin 157912 ▁caption 157910 فاق 157909 qhubeka 157905 uphi 157905 ▁occ 157893 วา 157892 ▁яҡын 157890 ▁twa 157889 ulet 157886 されて 157884 ztat 157879 ▁lọ 157879 尚 157877 ▁უკ 157872 ▁еди 157865 ▁sería 157864 ▁ಹೊರ 157859 ુલ 157855 ບໍລິ 157853 ▁offre 157852 امات 157847 deg 157845 ▁үйлчилгээ 157841 ▁spesi 157837 лих 157836 ▁داعش 157833 ▁Span 157828 vei 157826 ▁۱۳ 157825 nieku 157816 ▁pozna 157816 ▁kull 157808 elly 157800 หลวง 157798 ▁meglio 157793 ▁bind 157792 istí 157787 ▁210 157785 гэл 157783 覧 157783 ▁sikkert 157780 ▁high 157779 üsü 157777 ׳ 157775 ত্ব 157775 gare 157772 ▁тарих 157765 ▁हत्या 157757 ▁samband 157753 ھے 157752 ▁implement 157745 venir 157737 իա 157737 ▁екі 157735 ▁Wéi 157728 ▁ଗୋ 157723 ксу 157720 ▁وعد 157717 ▁Are 157714 ▁իրավունք 157708 ▁служи 157698 ▁celý 157696 ▁pwodwi 157693 ▁पू 157691 sien 157685 γνω 157684 dul 157681 ኦ 157672 irá 157671 ▁гости 157662 ▁menunjukkan 157659 厂 157656 ▁ಕಿ 157650 èrement 157633 ▁kaut 157629 飛 157629 ▁Berita 157624 ваний 157620 ICI 157597 ▁Dann 157595 ΕΙ 157593 ?“ 157591 ▁yun 157591 ธุรกิจ 157590 ▁שנה 157589 ሳይ 157584 ▁vī 157579 ಲ್ಯ 157576 ▁Angeles 157571 ▁projecte 157556 igne 157547 တြ 157545 ▁makin 157534 ▁الحق 157530 ညီ 157528 ▁अं 157521 ▁Porto 157520 áo 157516 ▁Lob 157515 ▁ưu 157515 рони 157513 ▁Jär 157499 ției 157498 ▁قض 157496 ▁ಹಾಕ 157493 ▁mater 157490 ▁governo 157489 ▁합 157487 ሉ። 157477 оне 157471 คู่ 157457 ▁Мұ 157455 ▁trebui 157454 2000 157451 ▁σχολ 157440 ▁Termin 157430 σί 157428 アップ 157426 ujejo 157421 ▁сель 157417 ▁fla 157414 ▁isaan 157414 ▁tək 157412 вот 157403 ▁vodi 157390 ▁СЕ 157390 Mat 157389 ▁پيدا 157386 暴 157380 ▁iska 157377 ▁koo 157373 ліп 157371 ▁lahaa 157369 ▁Freder 157368 fii 157358 7] 157355 観 157352 ▁niečo 157351 ▁संग 157348 bola 157342 ▁ilẹ 157340 qs 157338 GI 157333 ▁anque 157330 ▁называ 157330 ▁jaren 157328 funk 157322 hona 157321 álně 157321 ▁ещё 157321 সি 157309 ▁ਵਲੋਂ 157296 šao 157290 ▁seksi 157278 ▁школе 157271 ▁اشت 157269 liz 157263 ▁kuulu 157263 гий 157257 ▁sara 157255 ▁könny 157240 ▁excep 157238 ▁), 157227 ▁යාප 157220 ണമ 157210 릴 157203 ونکو 157196 ષ્ટ 157192 ▁өй 157190 લું 157187 นม 157183 寄 157179 лико 157176 ▁wall 157175 ▁terület 157173 ▁Konfer 157171 raž 157163 ▁zawod 157156 ▁ဒါ 157156 ▁kaik 157151 ▁reun 157147 ▁trud 157146 asie 157145 ▁туристи 157142 šin 157137 ▁поба 157134 ▁پئي 157124 ▁אתם 157123 ರೆಯ 157121 ▁बढ़ 157121 ▁அவர் 157119 老师 157109 ▁ಕರ್ನಾಟಕ 157100 ▁населения 157098 ▁عندما 157091 ▁levar 157087 ▁संविधान 157085 ▁området 157082 ▁торған 157081 ▁בבית 157080 ▁เท 157080 ▁KW 157079 ግር 157075 atten 157071 াইট 157071 ▁Drag 157070 ▁henero 157070 ▁exempel 157068 ▁puntu 157065 ຕັ້ງ 157057 ▁gjith 157055 ▁beszél 157045 ନୀ 157042 ਬਰ 157031 ▁საბ 157031 dala 157029 ственной 157025 ▁нигеҙ 157020 ວງ 157014 ▁पड 157014 ▁министрлығы 157008 ▁giản 156998 ારો 156996 ينة 156995 だと 156988 umą 156984 帰 156973 ▁Hoa 156972 ▁felic 156972 ▁tepl 156971 ▁любой 156968 载 156966 ▁sporo 156965 ってしま 156964 ▁plen 156951 ▁прес 156941 ..., 156938 ▁രാജ 156937 ▁Kuv 156935 Па 156928 watan 156927 ▁случи 156926 ального 156925 ▁pain 156920 ▁azok 156916 ørt 156902 ánt 156897 ▁१५ 156886 ▁kupata 156884 μαν 156880 /07 156878 ▁budget 156874 ▁ਕੋ 156858 ▁kõige 156853 ▁жағдай 156853 ▁програми 156847 ੁੰ 156845 mii 156843 ITI 156830 asil 156830 ▁hare 156827 ▁धर्म 156827 மைய 156821 ▁enorme 156810 ▁mamlakat 156802 ▁میلیون 156794 чето 156791 ▁බොහෝ 156790 意味 156774 znego 156773 ischer 156766 teil 156765 ▁Intel 156763 ▁INTER 156757 ▁وما 156754 ンの 156751 ▁Shqip 156750 ക്കുന്നു 156743 ▁байланысты 156734 兵 156725 ▁Dansk 156721 ▁viata 156721 ▁verdens 156717 ទ្ធ 156715 nið 156712 ▁имат 156709 හෙ 156694 ▁letter 156694 ▁väč 156693 ruik 156688 polit 156683 ▁ਰਾਜ 156679 ዎቹ 156671 ▁přip 156671 anske 156666 ▁استخدام 156663 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▁magas 156315 ობით 156314 ▁10:00 156310 hlab 156308 ضو 156308 тең 156296 ▁سياسي 156289 ▁бус 156287 itzeko 156285 ▁гара 156284 ▁pasāk 156281 tice 156276 ▁pär 156273 编 156271 块 156268 ństwo 156261 ریز 156259 ▁print 156259 ▁ሥ 156257 yse 156255 ▁potencial 156255 ▁ҙә 156255 แท 156250 ther 156248 ▁group 156234 ▁planeta 156230 ▁ذکر 156225 ▁медици 156222 ▁паў 156215 ▁vraag 156213 ▁ਅੱਜ 156205 ▁Irak 156204 ▁inscri 156204 ënner 156200 Je 156190 Video 156190 บุ 156186 梦 156180 ▁talab 156169 ▁sollen 156165 สมาชิก 156149 ▁ברו 156147 hra 156138 ▁1200 156134 ▁gwaith 156134 ünden 156123 ▁обаче 156122 2009 156119 ặt 156116 λύ 156113 ▁gama 156111 ▁formar 156108 ▁сайд 156108 ່ນ 156105 ไว 156100 ▁aghaidh 156093 าท 156091 ▁muž 156091 ▁Ark 156090 dv 156086 ▁fini 156083 /03 156078 ▁muid 156078 ρης 156072 ▁син 156070 ▁Lumpur 156064 ▁Categ 156061 ▁gemacht 156058 ▁bater 156048 imatge 156040 fí 156039 လြ 156038 ▁mjög 156036 ▁Нема 156035 ▁måde 156030 翻 156027 ισμό 156020 ▁tillsammans 156018 各种 155996 ▁Fen 155995 aquesta 155987 码 155987 ▁mı 155972 ಕರ್ 155969 ▁rost 155968 zug 155961 ▁Doch 155961 ▁бир 155961 ▁ממש 155945 ▁ओली 155941 ▁ambapo 155937 ொரு 155933 ดัง 155910 ੁਕ 155908 ▁yür 155895 hli 155892 ▁tungkol 155892 ▁centros 155890 ▁ажиллагаа 155888 less 155884 ▁ček 155883 вети 155875 ▁daje 155875 ▁wykorzyst 155871 ợ 155869 trans 155865 ▁повр 155860 ▁tegev 155859 ▁текст 155858 ▁ගා 155858 ▁műkö 155856 ▁ټولو 155855 ത്രം 155854 ying 155853 ilung 155851 ▁érték 155851 uul 155842 VIDEO 155840 ғыс 155830 ▁seinen 155829 ▁نع 155824 ▁өв 155822 ▁комі 155819 ましたが 155811 ▁práva 155807 ▁versi 155807 ▁Johannes 155798 ▁συνεχ 155794 ▁ਰਹੀ 155794 ንን 155791 ▁víz 155791 登録 155780 ▁հեռ 155779 τητες 155778 ▁hồi 155778 ▁adresa 155777 ikor 155772 kay 155772 ▁நினை 155772 சை 155765 当然 155763 ▁અન્ય 155761 ▁ауылы 155760 ▁عراق 155744 ▁odre 155742 ▁følge 155739 ▁мина 155738 ▁FL 155736 eningen 155735 ຕາ 155720 ▁математи 155718 مية 155713 ālo 155709 ▁වැනි 155706 ▁szk 155705 шите 155701 ▁መንግስት 155699 ▁Política 155698 edde 155694 тама 155688 eligt 155681 ▁natuur 155681 ▁новых 155681 ເຄ 155676 ▁dowladda 155676 ▁आयोजना 155675 alainen 155663 ▁نیا 155656 ብረ 155654 ▁стало 155653 ▁Album 155649 ▁сэр 155633 ▁элек 155622 omag 155621 ▁vannak 155617 ▁ທາງ 155615 ▁cier 155610 ің 155608 ▁Tad 155606 ▁kabi 155605 ▁dž 155602 ▁ύ 155599 ▁informacije 155590 ▁mës 155589 ▁голов 155580 ທິ 155564 ቤት 155564 ▁услуг 155556 ansk 155555 نز 155550 Ց 155548 ▁fjár 155543 ▁phambi 155541 itung 155539 ▁무료 155539 ross 155538 ▁пут 155538 ollo 155532 ▁area 155529 ▁خپلو 155529 ▁hjälp 155528 ▁kontra 155527 ▁қабыл 155525 ▁እኔ 155517 ▁xub 155516 либо 155513 ▁Scha 155508 єн 155505 ্ড 155501 ນໍາ 155499 ▁rive 155498 ▁टि 155495 KAN 155490 ന്ദ്ര 155485 idio 155478 ▁1986 155474 ▁kõr 155473 োম 155471 ▁možno 155471 ància 155461 larining 155459 مام 155453 ▁svět 155452 ▁دع 155451 ▁націона 155445 віта 155441 价值 155439 ▁증 155438 hlt 155437 ЭЭ 155433 ▁навча 155432 uments 155430 дий 155413 ▁σαν 155410 ▁נה 155407 ▁Royal 155406 ▁जन्म 155398 ළි 155393 ýr 155391 ρακ 155387 ▁sebanyak 155382 ▁Prez 155374 ▁praktik 155369 িকা 155360 ▁ਵਾ 155354 یدی 155348 ▁rut 155344 ಬೇಕ 155343 цом 155334 viť 155333 nö 155331 ▁bumi 155330 ▁տեղի 155321 ▁הבי 155321 ხი 155309 tij 155305 :50 155299 chilik 155293 ▁ուժ 155293 ▁комите 155292 ▁száll 155287 ▁lahatsoratra 155284 ینو 155282 ▁buena 155282 ▁digitale 155282 otis 155280 ▁konte 155270 ▁۸ 155270 द्या 155269 ▁ତାଙ୍କ 155269 ▁ղեկավար 155264 保障 155258 ▁odby 155251 怪 155251 ▁során 155244 ▁sih 155241 ştir 155240 ësisht 155237 ▁Başkan 155227 lechen 155218 ▁ข่าว 155214 ionn 155210 არო 155199 ▁tato 155195 зір 155183 ▁σκο 155182 гэ 155174 ▁Chí 155174 ▁greu 155166 ▁preda 155163 ▁Audi 155153 ryk 155149 ▁ल्या 155149 ▁olacaq 155147 arie 155143 lərinə 155138 ▁Direc 155135 ▁Ñ 155125 เพื่อน 155124 抓 155117 نام 155111 ▁вопросы 155102 ومي 155100 申 155095 ▁ජය 155085 ▁кейін 155083 akkel 155080 ▁kérdés 155078 əq 155077 ▁sekund 155077 楽し 155075 ▁πάνω 155074 ប៉ 155071 foto 155070 ίνουν 155068 ilat 155064 diad 155059 િકા 155055 ▁ludzi 155055 ★ 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154777 ▁নতুন 154777 ▁قل 154776 ▁ї 154772 െന്നു 154771 ▁girl 154768 ▁කියා 154765 ąż 154754 šej 154750 ▁Срп 154750 ▁بہ 154741 ▁zp 154734 ▁សហ 154734 цтво 154723 ▁hoxe 154722 ขาว 154720 ▁૨ 154720 ▁koli 154719 ▁Слу 154718 ▁त्यो 154718 ▁αξ 154716 ▁გახ 154708 ▁kosmet 154698 ственных 154695 ▁Васи 154693 heng 154692 ▁oluştur 154686 ▁выше 154686 协 154679 ügyi 154675 ▁-> 154675 quis 154669 ▁ايم 154668 ▁өте 154665 ナー 154659 щего 154655 ▁микро 154655 ▁lorg 154651 ▁ОБ 154651 யா 154629 ▁আমি 154629 ▁seminar 154624 ▁घोषणा 154623 ▁روا 154614 ▁سمجھ 154613 ▁sensa 154611 যোগ 154609 ▁dix 154604 ▁умет 154598 ▁celebra 154587 ▁мають 154584 ▁کراچی 154579 jaj 154576 ▁Lux 154575 իզ 154560 ▁alimenta 154557 ्रि 154555 न्ट 154554 It 154553 ▁अनि 154552 ▁vaig 154539 ጠር 154534 ▁család 154533 ▁propio 154530 ▁kaks 154529 lemente 154521 ▁මිනි 154520 wied 154508 レス 154488 tih 154483 ▁نفت 154480 μένες 154479 午 154479 ֿ 154469 tiz 154466 ▁הספר 154460 ▁მთავარი 154459 LT 154451 šča 154451 ញ្ញ 154449 ▁учеб 154448 ▁compra 154446 ▁American 154440 ▁中 154430 ▁revis 154429 جام 154418 льнага 154417 ينه 154399 umenti 154387 ▁verib 154387 ▁prijav 154377 ضل 154375 ▁braucht 154367 ▁ikaw 154367 ▁conto 154362 blem 154360 ennet 154356 이고 154348 ▁ofi 154337 ▁Main 154334 ▁төв 154333 astav 154331 每天 154328 ведения 154325 ▁;-) 154321 ▁líder 154321 ▁ABO 154318 ▁самом 154317 ាញ់ 154311 ගත් 154308 ▁Сара 154299 צמ 154285 ایش 154283 attu 154275 愿 154274 sung 154271 ▁Pinterest 154271 ▁دنبال 154265 nahme 154263 █ 154261 тых 154250 ▁wor 154245 ▁Bien 154242 ▁Jes 154237 ▁встанов 154235 igheid 154233 Net 154231 ▁Ол 154225 ▁زیادی 154218 ▁tinha 154214 stava 154202 ุ่ง 154195 :26 154190 ▁matur 154188 ▁हमारे 154186 ▁Национа 154180 ILE 154177 गल 154174 ecta 154171 ▁කාල 154171 ▁etmek 154167 mär 154164 кру 154160 ▁đôi 154159 ▁سولې 154154 ▁camb 154143 ▁GI 154136 वादी 154132 ọọ 154129 ▁Ain 154128 ळ्या 154126 丁 154124 sicht 154118 IRI 154116 стати 154115 ▁completo 154111 ▁빠 154108 ▁کوشش 154103 olás 154101 ▁junto 154101 ểu 154100 ▁ode 154099 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153840 ▁dạng 153838 过去 153837 کش 153834 ▁aceea 153829 luš 153828 ▁nacion 153822 ▁പറ 153819 ilea 153811 ▁încă 153811 になり 153811 χν 153810 dib 153809 ▁изјави 153804 ▁sall 153802 ▁ପର 153796 ▁CM 153795 ▁uklju 153793 脸 153789 ெய 153784 onim 153782 ่วง 153772 ēji 153764 drž 153759 谁 153758 थे 153744 ▁(199 153737 chting 153736 ▁noteik 153733 ▁sej 153732 ▁ND 153731 ▁ит 153731 לן 153724 ▁insa 153713 ାଉ 153712 ▁balay 153707 kanak 153699 จึง 153698 WO 153697 සක් 153696 콜걸 153696 ▁Rusiya 153684 xía 153669 ▁завр 153657 ▁તેના 153657 øv 153652 tart 153649 ▁ря 153641 ▁treg 153640 ▁професиона 153639 ஞ 153635 ▁yağ 153633 otar 153615 ▁Նախ 153610 ▁tuyệt 153601 ▁شاید 153598 nī 153591 ▁كتاب 153587 क्ता 153583 JU 153582 ▁Todos 153582 ▁וע 153581 ▁התח 153576 ▁Komun 153574 ▁وظ 153574 vera 153573 тау 153568 ,’ 153567 ▁terme 153567 ▁dobr 153566 ▁llibre 153565 ▁ಸರ 153564 ▁fontos 153563 ▁Sultan 153562 ној 153554 ក្រុម 153546 ▁ከእ 153542 ▁Lau 153539 டுத்து 153529 ▁ಇಲ್ಲಿ 153528 ▁یہاں 153524 летін 153523 ũng 153514 ▁Aff 153512 の一 153503 ▁тыуған 153502 ▁minul 153487 ▁enorm 153481 телство 153478 वली 153463 ▁mè 153454 ▁Japon 153450 ▁дур 153449 ▁گذاری 153449 ▁periodo 153441 箱 153437 niejs 153431 ▁៥ 153431 ▁၂၀၁ 153427 Ta 153423 żs 153415 ▁Eiropas 153415 ▁member 153415 ▁sidoo 153413 ▁Ë 153409 ▁þér 153409 ▁Oc 153405 mektedir 153398 ▁kapas 153392 ▁իրավ 153392 ▁თავის 153388 〜 153379 がない 153378 маты 153377 ▁새로운 153372 ikuwa 153370 plant 153370 ▁تین 153370 이라는 153366 amang 153364 amag 153363 ▁військо 153361 ▁forbindelse 153358 ▁влади 153358 ▁икенсе 153354 ▁التق 153351 ▁Gli 153343 ▁Prozent 153342 ▁Eller 153330 ▁αι 153330 ppet 153329 ▁определя 153329 лім 153325 േക്ക് 153321 ոշ 153300 சிய 153296 評 153293 ▁ouni 153288 父 153286 ▁nò 153284 ▁tecnologia 153283 ▁konnte 153281 jące 153280 gada 153277 ▁qul 153274 ческое 153272 ▁Wanita 153270 ▁vuos 153270 ▁progetto 153267 ▁sloven 153261 ▁Zer 153259 ▁Влади 153256 ▁Sj 153249 ▁labi 153248 ▁அதிக 153247 ▁መጠ 153245 ▁1987 153239 ▁ਪੰਜਾਬੀ 153235 jali 153233 ▁කතාව 153227 ▁FIFA 153226 ▁ured 153223 ▁sute 153216 지를 153216 inning 153212 ▁Зем 153209 ▁מנ 153209 ▁Taifa 153199 階 153199 ▁нази 153198 ΤΑ 153197 һыҙ 153189 累 153188 ▁ნი 153186 ▁φορά 153185 ▁café 153184 旅行 153181 bac 153176 សម 153173 ▁artır 153171 ▁ಬರ 153166 ▁поја 153164 etara 153160 ▁mið 153159 өт 153158 ಿತು 153158 ▁محبت 153152 ▁totalmente 153151 ▁поль 153146 ▁hd 153139 ▁követ 153133 いつ 153133 телна 153127 ാതെ 153122 izao 153121 ▁Cách 153119 ▁combat 153116 သည်။ 153109 ▁халҡы 153100 ingg 153099 ▁amháin 153098 ▁transpar 153098 ▁aktif 153097 ▁Ole 153095 ▁ઉત્ 153095 ▁elsker 153094 ▁máme 153094 巨 153093 iszt 153091 द्धा 153089 uzione 153088 ▁uğ 153087 ▁pobl 153086 ▁possível 153085 ସ୍ଥା 153080 แนะนํา 153075 ▁მონ 153073 ▁المج 153069 ▁stránky 153067 ▁how 153066 ▁odlu 153064 ▁одним 153061 ▁város 153059 礼 153059 ▁आवश्यक 153055 ligere 153053 рода 153053 培训 153047 šiem 153046 ▁рес 153033 補 153029 ▁altında 153026 מון 153025 xaa 153018 ▁Κυ 153017 ▁Mario 153012 ▁բաժ 153008 ensa 152998 ณา 152995 દ્ધ 152994 ພາ 152990 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152407 ▁देव 152406 ការណ៍ 152405 ▁луч 152404 ▁individ 152398 ▁аса 152395 ▁дирек 152395 ▁Regist 152389 ▁hansı 152389 ümüz 152385 ວິ 152384 ▁olunur 152381 ▁заради 152381 岛 152380 ▁शर्मा 152378 ေပၚ 152374 ▁käyttä 152373 hainn 152372 ▁rodi 152368 yyə 152366 ▁wap 152365 ▁երկ 152365 тельного 152355 ▁totiž 152349 :27 152348 ▁Москва 152345 ▁ਦਿੱਤਾ 152340 ▁Light 152334 ▁građ 152333 ▁соста 152332 显示 152332 ▁celu 152328 ▁odgovor 152323 ▁تنظیم 152316 ▁ngag 152315 øger 152298 익 152298 θει 152295 ▁afg 152295 timent 152287 ▁Erik 152282 λών 152281 ュー 152278 yttö 152274 ▁الرا 152274 ▁क्रिकेट 152274 ▁자료 152272 mete 152270 ▁geta 152270 ▁તેમ 152267 ▁Републике 152264 ғә 152260 ▁ameri 152258 ▁නමුත් 152258 тарын 152255 액 152247 iskas 152243 ែល 152235 ▁Abs 152235 nade 152233 ਮਾਨ 152231 ▁HIV 152228 ▁korral 152217 ▁ዛሬ 152215 ▁viņa 152211 ざ 152209 ▁dostęp 152205 ▁tits 152200 イル 152198 мых 152191 ▁anyar 152190 ▁хийж 152190 ▁कविता 152189 ekben 152188 ▁допомо 152187 实施 152184 ▁vinde 152181 ▁обов 152175 ulus 152169 мийн 152166 ▁ترکی 152166 성을 152163 yttä 152162 ▁нашите 152162 jpg 152156 ▁yox 152156 дено 152154 čnost 152149 ▁trava 152144 Ả 152142 стәр 152140 ▁bung 152127 čnosti 152126 ▁colec 152123 മുള്ള 152122 ▁alkal 152122 sed 152121 qur 152119 ▁Insp 152118 ▁ឡ 152115 ▁৬ 152110 ניק 152107 ▁sıra 152103 мно 152097 ▁انهيء 152095 লের 152083 ▁click 152079 То 152073 сила 152067 ▁poš 152066 ▁Фото 152064 ▁deti 152061 ▁کسب 152054 ▁nebude 152050 ▁ovom 152046 ▁капита 152045 sum 152039 ▁сказал 152031 طف 152028 ▁ניי 152026 ▁kojoj 152023 ▁blå 152012 经营 152008 ▁Здра 152007 ▁îl 152004 onych 151996 ▁პირველი 151995 ▁mulighed 151991 ▁৮ 151988 ▁١ 151986 ▁ଆମ 151984 -07 151979 ประมาณ 151977 idere 151971 ▁Көн 151970 tul 151969 GR 151966 etzung 151965 ▁ചെ 151965 ▁tingkat 151951 ▁जाते 151950 ▁katta 151949 ▁ukuph 151947 rih 151940 fed 151936 arsa 151933 ▁необхід 151918 iral 151913 ▁សែន 151910 ਤਾਂ 151906 ▁fapt 151906 ▁մրց 151905 ទៀត 151903 ▁beteg 151901 ▁مصن 151901 ▁incluso 151900 ▁spl 151896 런 151896 inding 151886 ▁чо 151882 ▁pist 151881 Dit 151878 ▁τώρα 151877 ▁Kinh 151875 幸 151872 ▁derechos 151868 େଟ 151867 ▁convers 151865 ▁دہشت 151859 ▁yiri 151857 ოფ 151856 ▁നിയമ 151845 ଷ୍ 151840 -199 151837 ▁rahat 151836 istika 151834 ▁অপ 151834 ▁Champions 151827 ▁ಮೆ 151820 mot 151819 ტრ 151819 ▁kezd 151816 edar 151813 ныс 151812 ▁ваше 151811 ▁კიდევ 151807 ▁taarifa 151806 ▁vais 151804 됩니다 151801 אלי 151788 经验 151788 ▁Miguel 151786 ▁méd 151786 ▁aste 151782 ▁сыға 151782 രേ 151780 ganti 151778 ▁Беҙ 151776 Ռ 151775 ▁внима 151774 ▁dalje 151768 ▁então 151766 ▁115 151765 сков 151760 ▁häl 151749 ועד 151746 reme 151741 ▁zaklju 151738 ,[ 151737 ▁åp 151736 ▁continuar 151734 ână 151732 даж 151731 ▁hunda 151730 ▁Py 151729 ▁isim 151729 ▁teren 151729 ▁содержа 151724 iget 151720 ษา 151717 ▁Dire 151717 ▁দেশ 151707 เต็ม 151706 ▁त्यांच्या 151706 ույն 151705 ▁sağlam 151699 ▁உலக 151698 0, 151697 ਿੰਗ 151690 據 151688 hta 151687 ലും 151681 eschen 151670 ▁müş 151669 هاي 151667 ▁Фо 151666 ▁Bandung 151663 ▁សុខ 151651 fahr 151647 ścią 151647 ходу 151635 ▁medida 151632 ples 151626 ▁דרך 151626 ánk 151620 ấu 151616 ▁سعودی 151606 ▁dup 151602 ▁Jah 151599 ▁علیہ 151599 ▁ял 151597 cic 151595 ക്ത 151591 ▁Lungsod 151591 ▁ដូច 151587 ▁lebo 151583 ▁жоба 151582 ▁والتي 151580 ▁Gusti 151574 ▁więks 151574 ▁ciddi 151554 ▁ची 151553 ▁Popular 151551 ▁უკვე 151550 τιά 151542 پن 151539 ▁vazhd 151533 ekar 151523 ▁رض 151521 úna 151519 nab 151516 ▁Breizh 151515 ▁ଯୋଗ 151511 ▁taong 151503 ▁ບັນ 151498 ন্ড 151494 ေရာက္ 151493 ▁наз 151490 rodu 151485 ▁Vod 151480 ▁oshir 151478 ▁Dah 151464 ▁Sci 151459 ▁edildi 151459 ▁1919 151452 fia 151450 ▁Тә 151449 ▁putin 151445 ▁ഉപയോഗ 151443 ኞ 151441 ▁membaca 151440 вары 151438 чается 151437 ▁meist 151436 相手 151426 ▁১০ 151423 ▁Ish 151418 ▁escu 151415 ▁Klas 151407 ▁technik 151407 ▁አው 151401 ▁güven 151400 Mu 151399 sig 151398 ▁γρα 151397 ገል 151393 ▁Тэр 151392 ▁trì 151390 ▁රකාශ 151386 ▁EG 151384 ▁Click 151381 ▁tekem 151370 iteter 151366 ▁əm 151363 ▁указа 151356 ション 151356 ▁titul 151344 ▁lihat 151342 ▁аба 151339 ▁хужалығы 151327 ▁Agar 151326 ▁ज्यादा 151326 ▁च्या 151325 стом 151312 ▁tengo 151308 akir 151307 ▁դեմ 151305 ඛ 151303 гын 151302 нять 151292 ▁гэтага 151292 ▁ଦୁଇ 151290 ▁calcul 151289 ▁присут 151288 :" 151283 kid 151280 पर्ने 151275 ▁הצי 151271 றிய 151268 ▁stø 151265 ætti 151260 dern 151258 ▁ndod 151256 ▁particolare 151254 史 151253 ▁тыш 151250 ▁Xalq 151249 ▁ਬਾਰੇ 151248 iaal 151246 ▁Кур 151245 انية 151240 ▁body 151237 маҡ 151233 ғас 151233 тать 151230 ▁přij 151228 ▁Nog 151227 ▁entà 151221 ▁shak 151217 ▁üzerine 151213 тельный 151208 混 151207 혁 151201 ▁управління 151200 ▁রো 151199 qiq 151195 ▁वाच 151188 import 151185 եգ 151184 ▁viņš 151176 ੍ਹ 151166 መራ 151163 ▁ruta 151143 ▁Cabdi 151141 ▁Jav 151139 ▁Neb 151135 ▁aban 151135 ▁arkadaş 151135 ▁jä 151130 ners 151114 /. 151109 لن 151108 决定 151108 ▁форум 151095 ებისა 151082 ▁yalnız 151082 ▁આજે 151082 ურს 151081 ▁femin 151069 ানে 151067 ▁tilb 151065 ▁yali 151065 িপ 151061 可是 151061 ▁какие 151055 alha 151053 ▁Eva 151045 訪 151043 ▁YANG 151041 ▁хэрэгтэй 151037 ▁مدیر 151034 ▁המי 151033 ▁nabíd 151029 ▁quest 151027 ▁jalma 151024 ▁보고 151023 ▁baju 151021 ADE 151019 ▁Run 151017 ปรับ 151015 ▁NOV 151015 ▁ഡോ 151013 ▁DNA 151011 ▁rekening 151009 ▁efek 151002 klus 151000 šne 150992 рім 150985 ampan 150981 ekh 150980 ▁deriv 150979 ▁меч 150979 ▁برس 150972 ▁besoin 150968 णाऱ्या 150963 略 150962 alnya 150960 രിക്കും 150955 ▁адрес 150954 ▁ցուց 150953 ▁Sop 150950 menu 150945 ចេញ 150945 ਲਾਈ 150944 noz 150940 ▁classe 150935 ▁вариан 150932 aquest 150926 ▁Ihren 150926 ▁осіб 150920 cra 150917 /01 150915 ▁guz 150912 ▁applic 150908 ▁هېواد 150908 ոք 150901 ▁१२ 150901 ੂੰ 150891 был 150880 ▁Bắc 150873 缺 150873 etic 150872 ▁ഭാഗ 150856 ாலும் 150854 ▁imkan 150854 шњи 150853 ▁ear 150848 ▁Gil 150846 2008 150845 ▁ಕೆಲಸ 150841 вание 150837 ▁jijini 150837 ცე 150836 ▁FB 150825 ຜົນ 150822 ▁झाला 150809 χυ 150807 ▁conos 150807 MB 150805 ▁احت 150804 ▁ตัว 150799 ▁tästä 150798 ▁등을 150796 ▁ଭୁବନେଶ୍ୱର 150794 ▁spørgsmål 150787 ▁چهار 150782 ▁Además 150775 قول 150764 iranja 150758 mee 150757 mik 150757 īk 150748 ້າງ 150748 ellas 150720 ficación 150720 ▁Kaj 150714 ▁wajah 150703 ▁tedy 150695 untar 150675 ▁hittar 150675 ▁khusus 150670 ابت 150662 adet 150659 ▁ikka 150658 ▁жүйе 150645 alem 150644 ▁започна 150643 ▁VIII 150642 tief 150637 ნიკ 150637 聯 150631 ▁Bev 150628 wahl 150626 ▁viš 150625 țe 150620 페이지 150613 đe 150612 しております 150608 ▁хоҡу 150607 ▁SIM 150600 vių 150596 വാസ 150593 ▁hadis 150590 ▁lakho 150588 ▁agam 150582 ▁მოხ 150582 ▁адбы 150575 形成 150565 ▁Baba 150558 vais 150555 ▁односно 150554 τικός 150547 ▁ياد 150547 /05 150542 ▁finder 150542 ▁portugu 150541 ▁KB 150540 ▁Pac 150528 ▁kode 150527 ▁januari 150523 ▁sóc 150517 ▁brun 150513 ▁bizony 150509 ▁информацию 150507 ちょっと 150494 ▁nummer 150489 ▁mellor 150482 ▁गीत 150477 ▁Tamil 150472 融 150472 เคร 150467 だから 150464 redo 150462 ▁могли 150458 多的 150458 ▁fili 150444 leen 150440 ▁aikaa 150438 дарын 150429 ື່ອ 150427 ifen 150426 ੜਾ 150418 ▁lau 150415 ตรี 150413 有人 150413 ▁ნახ 150410 ғары 150407 ືອ 150395 ▁slim 150393 ▁biag 150391 市場 150389 ▁× 150385 ▁kenkuana 150383 ▁davant 150379 ▁၊ 150375 效果 150375 ▁ભા 150369 ▁दूर 150368 ▁ارد 150366 ▁පිරි 150364 ▁avem 150363 ữ 150358 ▁فارسی 150355 ▁عب 150354 äck 150349 ▁Plas 150347 ▁പഠ 150347 ▁LCD 150345 ▁lage 150345 ▁дэм 150343 ാണ 150342 ▁modal 150330 ▁የኢ 150318 ▁사실 150315 حل 150312 ▁Moz 150305 ▁Merc 150303 ▁февраль 150302 izza 150300 ▁نگاه 150299 ▁Leo 150290 ▁ghi 150289 ▁Ше 150283 ტურ 150281 2). 150278 ▁piln 150276 ▁pogod 150276 ▁promet 150276 ▁sipas 150269 ▁liy 150265 ▁환 150265 ຖືກ 150263 ▁blogger 150263 kast 150256 úng 150255 ▁násled 150250 unat 150245 adav 150243 ▁kautta 150242 anning 150239 alad 150238 ▁៤ 150228 ▁Lake 150221 进一步 150219 ▁ና 150218 ằng 150217 였 150217 िती 150215 хаар 150214 ಸ್ಥ 150213 ▁دفتر 150213 руу 150204 ▁industria 150194 ▁प्रतिक्रिया 150192 ▁തല 150187 elec 150182 ▁बारेमा 150179 грам 150172 ▁Elektr 150160 ▁онда 150156 ΙΚ 150155 ▁terakhir 150149 zyst 150143 ▁бағы 150137 ▁investig 150136 ▁әлеуметтік 150134 ▁dikkat 150131 ▁እንጂ 150130 ▁جاء 150129 ismer 150127 ▁pozosta 150116 oton 150112 ▁beek 150112 ሰማ 150111 ▁volume 150099 ▁fum 150096 찬 150096 ▁llam 150088 ▁vero 150087 材料 150087 work 150080 ▁быць 150074 ▁lini 150073 ▁Iar 150067 करी 150063 arax 150059 ▁Spo 150058 щей 150047 ▁والح 150047 ▁investigación 150037 ▁اهم 150037 ខ្មែរ 150034 ▁vieš 150033 ்கு 150024 รอง 150023 dl 150019 රට 150017 ▁aproape 150014 ▁ទទួល 150012 եպ 150010 ▁egingo 150008 ▁4.0 150004 ▁ремон 149999 table 149993 ິ່ງ 149988 ▁тапҡыр 149987 urkan 149980 ▁Kanak 149980 երն 149973 ▁başka 149973 விட்ட 149962 abs 149961 arz 149959 इस 149959 ▁ezért 149957 وش 149951 ▁nags 149948 ▁Thiết 149944 ▁Maq 149942 chaidh 149940 зите 149934 ▁бие 149933 ▁photos 149925 ▁cone 149923 ුවට 149918 ▁бяха 149918 ▁पढ़ 149914 ▁Sens 149909 ▁mmadụ 149899 ▁belirt 149882 תן 149881 zion 149872 дән 149872 ▁ورود 149870 เธอ 149869 ▁avis 149864 stitu 149860 uksesta 149854 ▁տար 149847 деле 149841 주의 149840 ▁हूँ 149838 بية 149837 ▁באר 149834 ಣಿ 149828 ▁belang 149827 äre 149825 ▁сучас 149824 ▁yl 149819 ०० 149817 hlás 149815 ▁свър 149814 ujemo 149813 ▁2019. 149813 weli 149812 uara 149810 ▁झाली 149808 ▁exista 149798 ▁ўз 149797 ▁tədbir 149796 ▁oed 149794 ▁fala 149787 ▁lokuthi 149779 ▁olivat 149779 ندس 149778 ▁yüksək 149774 ▁Также 149774 طور 149773 ▁ಕಾಲ 149772 ▁رشته 149771 ▁غلط 149764 ▁Tallinn 149763 ▁الإنسان 149762 ▁lager 149761 ▁Victoria 149758 ланд 149753 νοντας 149751 ▁muud 149751 sii 149750 เกี่ยวกับ 149750 ▁yem 149749 ▁սկս 149746 ▁yetu 149741 ಿವೆ 149730 zwa 149729 ▁جمله 149725 ▁2007, 149720 ▁trắng 149717 ▁14- 149715 ▁فیصلہ 149714 ▁mengetahui 149710 ▁qə 149709 ▁qilib 149704 เลือด 149699 ▁осу 149688 ▁shum 149685 चर 149684 ▁दिने 149683 ▁descar 149677 чких 149674 ိဳး 149672 cita 149669 ਪੁਰ 149655 ତ୍ର 149654 ▁чолові 149652 बाद 149650 ahid 149645 ▁πρώτη 149644 ▁منځ 149641 ▁Спа 149639 ▁həyat 149628 ▁GL 149627 isla 149626 ▁ALL 149624 ენა 149621 。, 149616 acağ 149612 omt 149606 ალის 149601 ▁diferente 149598 ପତି 149595 ▁basta 149591 ▁gerade 149589 私は 149588 ▁memoria 149585 ▁metais 149584 ▁pian 149578 기가 149575 apta 149573 खि 149572 ▁cim 149572 ▁chy 149571 តាម 149570 ▁utilizar 149570 ▁ugyan 149554 ▁тему 149552 abaw 149551 ▁andro 149551 ਗਰ 149550 ถ้า 149549 ԵՐ 149547 jón 149542 ▁Tačiau 149541 زيز 149538 ▁طالبان 149535 ▁365 149532 這樣 149517 环 149513 ▁samtidig 149512 ▁وأن 149508 ▁خلا 149503 ▁siyasi 149496 ச்சி 149494 uję 149485 ▁profi 149481 סם 149477 澳 149465 ஹ 149457 ▁luwih 149455 ▁нашата 149455 分享 149455 ▁ane 149453 եռն 149446 ▁ЕС 149446 ヒ 149445 ссии 149444 ▁Gad 149444 চে 149443 ▁siamo 149443 ▁तत् 149442 ്യം 149441 sab 149437 ▁Pie 149436 ▁seguro 149436 ▁similar 149436 σταση 149432 ▁fig 149431 ▁jotain 149429 ▁üzerinde 149425 ▁miljø 149422 ہم 149420 cement 149411 ▁জাতীয় 149408 ▁maakt 149407 ▁մենք 149394 ▁ډېر 149391 imui 149389 ▁ಕೂಡ 149383 패 149382 երթ 149365 ោល 149365 ▁இர 149365 ▁Loch 149350 ▁Luft 149347 ▁заң 149347 चन 149344 পর 149333 ▁აკ 149332 pret 149330 ▁дахь 149327 ▁metode 149316 ▁compro 149312 ▁pues 149310 /- 149303 ningu 149302 ábor 149291 ▁വായി 149287 ljanja 149267 电影 149266 eshme 149262 ▁бидат 149262 ▁petite 149256 ▁bajenn 149255 മാര് 149254 ▁eind 149239 ▁EC 149235 ▁necesario 149233 asida 149228 әнә 149223 ၿပ 149222 ffet 149219 ▁କାର୍ଯ୍ୟ 149219 ▁કહ્યું 149217 ▁Халыҡ 149214 ▁Nghymru 149208 ulun 149207 ▁τρο 149202 ▁прад 149201 เปลี่ยน 149198 jenja 149195 ũ 149189 ▁йөрө 149177 אַנד 149175 ▁maayo 149159 ▁ਡਾ 149158 plet 149157 1), 149155 ▁uSomandla 149154 ▁nghi 149150 !」 149145 க்கிய 149143 ytä 149140 ▁ලංකාවේ 149135 جود 149133 သို႔ 149133 ເດ 149125 ▁Θα 149120 ▁find 149118 楽 149118 واج 149114 ▁Така 149114 ▁panahon 149111 ▁pà 149110 ▁Før 149090 ▁ମଧ୍ୟରେ 149086 ተር 149085 වූ 149080 ▁business 149079 ▁Rog 149077 ▁ਲੋਕਾਂ 149067 ▁unit 149066 elit 149065 ▁Giklaseklase 149057 ▁करू 149050 برد 149045 ▁sigu 149034 undang 149032 timet 149025 ▁Zum 149015 ▁pozor 149010 ម្ម 149006 えば 149005 悪 149002 ေဒ 148990 ▁শুরু 148984 ▁spolu 148982 ▁seguridad 148980 ▁uşaq 148977 ▁sembla 148963 ▁ඉතා 148960 ▁निक 148954 ▁mme 148946 ▁tercer 148943 ▁dokumenta 148940 ▁hüquq 148939 ikes 148934 orija 148926 ເຈົ້າ 148925 ▁Hind 148924 альных 148922 ማር 148914 lea 148912 :35 148909 တွ 148906 біль 148902 appen 148899 ▁ubu 148899 cionado 148892 顔 148889 ▁удаа 148888 ▁kondisi 148887 ▁оролцо 148881 ▁११ 148875 erede 148874 bero 148863 ▁ё 148863 icken 148857 ▁ulwazi 148852 ▁hozz 148850 ▁Modern 148846 ਝ 148840 /08 148837 ▁complex 148832 が必要 148832 ▁principales 148830 коли 148824 ▁kualitas 148820 ▁lär 148820 ▁වෙනස් 148811 zip 148809 ີຍ 148809 שער 148807 ාර් 148806 ▁rab 148806 iyya 148805 ▁Santo 148803 tuje 148802 াতে 148795 essaa 148779 ▁nx 148778 ▁ऐसा 148772 ▁celle 148771 ▁lata 148757 ▁oby 148749 ▁commer 148746 တူ 148745 fana 148740 ▁данных 148740 ▁오늘 148739 ▁промени 148738 ▁situation 148735 พื้นที่ 148734 ▁һу 148731 ▁segon 148728 nac 148727 ▁Trav 148722 ▁powied 148722 ▁tiba 148714 ▁meas 148713 ▁киң 148713 ▁jeder 148699 ▁partit 148693 ブル 148693 ▁फेसबुक 148692 ATU 148686 ▁code 148681 ▁විශේෂ 148675 ▁Jug 148673 antaran 148670 ▁ठे 148669 แดง 148666 ▁хуулийн 148661 kosi 148658 ▁ფილ 148649 ▁1,2 148626 ▁aš 148621 ▁παιδιά 148620 ברה 148618 쟁 148614 xit 148611 ētas 148608 jaus 148607 ギ 148606 сінің 148605 бре 148604 ▁ହେବା 148603 ▁ඉතින් 148598 дение 148593 ▁bete 148578 づ 148578 ▁गर्नुहोस् 148574 ▁پروژه 148573 ▁ਵਰ 148573 umes 148572 ▁Россия 148566 ரின் 148562 istic 148558 ▁আমার 148553 ാരി 148548 ▁رضا 148547 ▁결 148542 ▁оти 148540 ▁respecto 148536 ктора 148535 вийн 148534 лес 148525 ▁mobiele 148525 ▁ہوتی 148525 ▁istəy 148514 ▁žena 148512 ứ 148510 URI 148506 ذكر 148506 ▁سای 148502 ordi 148500 ▁gamot 148497 ▁चला 148491 ▁sef 148487 bio 148486 ▁volont 148484 Str 148479 ▁всіх 148479 胜 148478 ▁ຢ່າງ 148477 ▁Πε 148474 ▁ဂ 148473 वले 148472 ▁patrim 148469 ▁அரசு 148464 ▁Domin 148447 ▁ऐसे 148447 ρός 148445 ▁lingu 148443 ଣୀ 148441 वण 148439 ▁millor 148439 ▁સી 148439 ▁reading 148430 ▁kū 148429 ▁:( 148427 ▁varētu 148424 ēmu 148423 хон 148417 ▁toa 148416 ▁դա 148416 ▁стандарт 148413 ▁नी 148406 kovi 148405 ▁mln 148403 ▁intu 148399 mez 148393 ▁৭ 148392 jalo 148388 ▁tertentu 148388 ▁cyk 148386 ▁kuh 148383 ▁Германия 148381 ▁seur 148367 ▁manfaat 148354 ▁اسلامي 148353 ▁PARA 148350 ▁프로그램 148350 ण्याची 148348 陈 148344 ೇಕ 148342 られた 148341 ▁กร 148339 ▁গ্র 148337 ्यान 148324 ilige 148322 Europa 148318 endr 148314 ▁ചി 148313 ▁ویب 148311 ideach 148307 ichi 148306 ▁kær 148301 یب 148297 isio 148296 ▁ڏئي 148294 변 148291 ▁filtr 148289 ▁Vì 148282 ønn 148277 ▁අමාත් 148276 ▁hvað 148275 ▁ғана 148268 ría 148266 ▁danych 148264 ▁Συν 148262 ▁aktual 148260 ▁médi 148257 ቀው 148256 ▁variant 148252 ▁Ђ 148252 ಗಾಗಿ 148250 ▁படி 148249 ▁tillbaka 148244 കെ 148243 ▁सामाजिक 148242 ုိင္ 148234 ▁leva 148233 ▁comhrá 148231 ▁அமை 148225 চিত 148224 シャ 148220 bla 148211 ▁algun 148208 ▁Zab 148205 ▁produkty 148201 ▁Día 148200 chean 148197 ▁pirms 148194 меня 148193 ▁kaaduan 148188 ▁Cant 148185 lägg 148183 ▁ука 148183 ▁Боль 148179 ▁dòng 148177 ▁ஏற்ப 148174 ătă 148163 ▁κύ 148162 »,- 148161 ▁yaşı 148161 ሆ 148160 策 148155 ▁inser 148151 ▁Kans 148147 ▁laiku 148147 וחות 148145 काल 148143 ড়ে 148140 Ве 148139 ▁tiszt 148136 enser 148132 chem 148123 ▁rekao 148123 ▁intelig 148115 ▁Soc 148111 ▁201 148110 ▁بودند 148099 ங்கி 148096 ುವುದ 148094 轮 148094 ▁umat 148089 ▁ашигла 148088 ▁gusta 148085 ▁Свет 148085 ▁Water 148077 ્યાં 148070 ページ 148052 ົ່າ 148048 側 148041 显 148027 ▁tind 148025 ഴു 148019 ▁nimic 148018 ▁गरि 148018 ▁её 148016 ▁ಮೂ 148013 ▁dësem 148001 ▁classi 147997 ītu 147985 ▁lyst 147983 ▁המל 147982 xica 147979 laub 147966 ▁Nt 147958 ▁osp 147958 ▁бүрт 147956 urri 147950 ▁MAL 147950 ▁ക്കും 147947 ▁ទេ 147943 ▁века 147941 öp 147939 ▁redz 147939 यात 147933 eleza 147929 ▁väg 147928 odes 147920 ▁ਮਾਰ 147918 astan 147909 েজ 147909 ▁cura 147908 ▁ngokuphelele 147907 ေအ 147906 ▁periode 147897 ibat 147896 ▁Tuhan 147895 ▁clas 147895 orul 147881 ▁ከዚህ 147879 ▁ზე 147877 ▁광 147867 ▁fondo 147862 ▁Dak 147861 ▁repet 147859 ಮೆ 147858 ▁impuls 147858 ▁заа 147858 ▁Тө 147855 antay 147854 ▁skladu 147851 عتبر 147841 لاء 147839 ▁voidaan 147836 ▁आयु 147832 ▁තියෙනවා 147832 आर 147824 rọ 147823 ▁Gå 147817 护 147816 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▁unserem 145451 ▁آگ 145449 ezon 145448 ▁Registr 145447 اید 145428 ▁maan 145428 ▁море 145423 चाल 145422 ybos 145416 ▁הרי 145414 cre 145411 ällt 145411 ▁بور 145410 ▁зазна 145398 ▁Nigeria 145397 ্লা 145396 ▁બના 145395 องค์ 145393 により 145384 ▁finnes 145381 ▁ausge 145376 ▁புதிய 145376 ▁Hrvat 145367 ▁Taf 145359 ສໍາລັບ 145356 것 145348 ▁fuld 145346 ▁болса 145344 ▁fenomen 145343 ▁kogu 145343 ▁[[ 145338 ▁одре 145337 ▁ਤੱਕ 145335 ▁ქართული 145332 holelwa 145331 ▁بھارت 145315 ▁անհ 145297 ▁besl 145293 かか 145288 ус 145285 ▁מנה 145283 ▁blond 145278 ▁mại 145275 ಕ್ಸ್ 145274 utul 145259 ▁song 145258 annak 145256 ▁ù 145255 ▁juste 145253 itā 145250 ▁måske 145245 ▁सांग 145239 ▁tř 145236 ▁หนัง 145235 Mod 145224 ▁Rest 145224 ▁халық 145223 ▁impos 145222 ▁lyd 145214 ▁Hid 145212 ▁meil 145212 리아 145200 ▁축 145192 ▁Majlis 145191 ▁fedha 145187 ▁Wh 145185 ▁tô 145184 ▁Programm 145182 ▁kapac 145182 ulares 145177 TB 145175 ▁වෙත 145174 ättning 145167 ▁முன்ன 145167 ▁brig 145159 ▁पार् 145159 uruh 145157 ▁estou 145152 כב 145150 ▁Date 145145 ▁lähet 145144 ▁volte 145144 ▁bera 145143 եւոր 145141 āku 145137 ▁ABD 145135 ▁Кара 145135 ▁Chúng 145127 บิน 145126 ▁szkol 145124 ▁minyak 145122 cus 145120 ▁Lees 145120 ▁kartą 145119 ▁хәл 145118 ropa 145116 ectu 145113 होस् 145111 ▁aktivitet 145111 흥 145108 ระหว่าง 145103 ▁milyen 145102 lighed 145098 ▁کنار 145096 ëtare 145094 ▁другим 145094 -21 145088 ▁мала 145082 mia 145075 ▁(( 145072 ▁Sign 145067 ▁ngoại 145067 ▁google 145066 ▁супер 145060 ▁شنبه 145052 itaa 145048 ▁municipi 145048 داء 145047 ▁liitt 145045 ▁முத 145044 ▁meiner 145042 ▁તેને 145039 ▁Svar 145038 ▁veter 145035 ▁PE 145032 ▁kron 145028 ▁பார 145026 こちら 145021 stri 145020 järjest 145012 iczne 145007 ▁ngemuva 145006 ▁arası 145005 ছিল 145001 ▁ਭਾਰਤ 144997 ▁ਬੇ 144994 ▁cuideachd 144990 ▁कुमार 144987 ▁sellest 144985 ▁huj 144982 ▁mav 144982 ▁ndërsa 144974 ishda 144973 ρηση 144967 ▁bja 144967 ▁Io 144963 ▁Kuna 144959 ▁kahit 144954 گران 144950 վա 144946 ảo 144943 ▁kupitia 144942 事業 144941 ▁spør 144937 ▁mih 144935 ▁Road 144933 ρίες 144931 ▁ටික 144929 ▁λι 144928 utuu 144919 කම් 144918 ▁تبدیل 144918 kili 144914 ▁banc 144903 录 144889 ▁maig 144888 éir 144883 ▁Oleh 144883 ▁dirix 144882 ▁Совет 144880 вниот 144875 ▁lisäksi 144873 ▁ښه 144873 ▁queste 144867 许 144867 разуме 144859 śni 144852 ▁私 144840 isation 144834 ▁Cô 144833 hëm 144822 ▁pemerintah 144813 ▁хе 144807 ▁orde 144804 մի 144801 شل 144798 лэлийн 144793 ▁займа 144791 ▁түсі 144783 特別 144783 ▁odl 144782 ẽ 144778 ▁Oktubre 144775 ▁úsáid 144772 ▁преко 144772 ▁उपयोग 144772 ebil 144769 ▁koliko 144769 ▁faoliyat 144759 ▁ولي 144755 ▁kultura 144753 Ė 144747 ▁сығы 144746 ▁osv 144742 tido 144728 ▁কর্ম 144728 ด์ 144727 әйе 144723 ▁bap 144722 ▁Bergen 144719 ADI 144717 ▁ឧ 144716 ▁ඔයා 144711 әсә 144700 ▁харак 144700 ▁сотрудни 144699 ▁Final 144695 ସନ 144693 ▁הלי 144689 ätä 144686 crit 144684 τερ 144678 เดิน 144672 ▁conclu 144671 elako 144670 :29 144666 amientos 144663 ▁وطن 144663 ▁nuevos 144662 เอก 144658 ▁najbol 144657 istent 144653 ▁krijgen 144652 ▁эле 144647 麻 144641 :05 144639 ምና 144631 ėtų 144628 ည်း 144619 ▁көс 144606 köz 144602 ▁bayyana 144598 မ္ 144597 ▁kwalokho 144594 ▁okoli 144590 ▁rhai 144589 ▁düny 144576 ▁മാത്ര 144572 ত্ত 144571 ຶກ 144566 大切 144565 kea 144559 ▁tenho 144557 ▁χω 144551 ▁fejleszt 144545 ▁fundamental 144544 ၀၀ 144543 agana 144539 ▁verzi 144538 不同的 144531 чките 144527 ▁düz 144526 ▁партия 144521 handel 144517 isher 144512 ▁ცხ 144507 ▁დაგ 144496 луы 144489 ▁أف 144488 ▁suy 144485 ▁lopp 144481 ▁matemat 144480 ▁уч 144478 ▁соција 144477 ▁svojih 144471 ▁odam 144470 ▁endnu 144469 ▁טע 144465 ▁szív 144461 ▁व्हा 144459 oline 144453 ennus 144449 atua 144448 ▁කියන්න 144448 ▁consumo 144446 ▁Vinc 144445 шої 144439 /04 144438 сек 144436 ▁servici 144433 iyati 144431 ▁дав 144431 ▁112 144424 ▁luna 144424 ▁سرمایه 144417 ווער 144416 êl 144412 ▁amesema 144412 ▁pup 144411 ▁Restaurant 144410 ▁dde 144405 ▁آل 144395 mér 144393 ▁sze 144392 صال 144387 ዲስ 144385 ▁goufen 144380 ▁Cep 144378 ▁lệ 144377 ▁yokuthi 144373 ysel 144364 κια 144355 вном 144352 ▁Trường 144351 ▁Semua 144349 ▁Ry 144347 ▁מקצוע 144347 ΤΟ 144344 ▁sidlakang 144329 ▁novembra 144321 ▁tuyển 144320 ▁ακόμη 144319 ottaa 144317 ▁причина 144310 HP 144305 ▁Bry 144305 ட்க 144302 ходят 144299 ▁ecc 144298 emaa 144296 ýš 144295 ▁gooto 144291 غان 144283 wei 144281 ▁quoi 144279 ▁lenne 144277 inam 144276 ΡΑ 144276 ▁논 144275 英国 144271 ложение 144269 ලින් 144266 ▁trabalh 144263 ług 144259 ▁rein 144250 πω 144247 риста 144244 େଇ 144244 ▁imam 144242 ▁освіти 144241 lý 144231 masının 144225 ▁srp 144210 ▁اروپا 144209 üyor 144208 ▁действия 144202 နည္း 144201 ادية 144190 landi 144189 ukeun 144188 kurs 144187 ໄຊ 144186 നേ 144177 ▁đoạn 144174 ▁Journal 144164 ▁ହୋଇଛି 144147 стью 144146 ວມ 144145 ▁कप 144141 τικ 144136 ▁unig 144132 ▁பாட 144130 ▁sella 144124 込み 144122 ▁ورزش 144110 ▁esco 144108 ▁국내 144102 იძ 144100 ▁twitter 144100 ▁оро 144097 ▁televis 144086 ▁งาน 144085 ▁Klub 144079 ▁Walay 144073 ▁wenig 144072 ▁kring 144070 ▁enligt 144069 ▁माल 144068 ▁सहित 144066 ▁ህዝብ 144064 ərək 144059 海外 144057 त्रा 144052 ▁bont 144048 стно 144046 ▁использова 144044 ▁zug 144043 ▁cộng 144039 ர்களுக்கு 144036 ▁انسانی 144035 шее 144029 ▁Geme 144023 μένος 144022 ▁Now 144022 сор 144011 äär 144010 ▁muitas 144010 állítás 144008 ▁Xu 144008 ▁distrito 143997 進行 143997 पर्छ 143995 ்பு 143994 ▁bën 143993 ▁mutu 143993 ρευ 143991 aktan 143989 Ұ 143989 יינ 143986 ▁Archives 143986 ▁kārt 143985 ENI 143982 ▁aplika 143979 ▁olunmuş 143977 iddel 143975 ▁sank 143975 ▁Алек 143975 ▁საზოგადო 143975 證 143975 ▁Brand 143966 ▁λόγ 143964 ▁Copa 143963 ндай 143960 туре 143958 ▁qiy 143956 ▁mayu 143955 Port 143952 ▁sull 143951 ▁kwem 143945 чнага 143942 ▁clients 143938 ▁Ευ 143928 そうです 143928 नंतर 143925 ▁صحت 143915 ▁introduc 143910 ▁sollten 143902 ▁любы 143901 ▁uspe 143899 ערן 143898 ർത്ത 143892 :31 143890 ▁Prima 143890 ୈ 143880 зации 143878 ▁doe 143878 ▁компа 143873 ▁khỏi 143872 eries 143870 ▁alisin 143865 ▁juht 143864 izh 143862 ▁Metal 143860 голе 143850 ▁nostres 143844 ▁포함 143840 ▁نگ 143839 도록 143833 jska 143831 ▁சீ 143831 dee 143827 ឧ 143824 ▁skrive 143815 聲 143802 ▁XII 143801 ѳ 143798 чены 143796 ▁тарихы 143787 ▁pèr 143786 êre 143779 ▁احتمال 143778 ▁Lec 143775 ▁ishin 143775 ▁aeg 143766 ▁tíma 143765 ▁kule 143760 ▁ଟଙ୍କା 143757 ▁LO 143746 ାଇବା 143745 ▁баға 143735 nutie 143727 ▁sengem 143723 ▁ulike 143709 ▁thiếu 143707 ▁Бога 143702 ▁foron 143697 ▁තමා 143696 ▁декабрь 143695 рю 143694 ▁aliy 143694 財 143691 Eng 143686 akwa 143681 irsiniz 143680 ▁Анг 143680 ashin 143674 siooni 143673 érieur 143662 ▁שפ 143659 ېد 143655 ▁υπουργ 143651 oire 143650 ▁урынлашҡан 143643 ▁sidan 143642 ▁миллион 143642 ▁ақпарат 143636 ثير 143626 ▁нами 143611 ୍ରେ 143609 ▁проблемы 143607 ▁4000 143603 ▁୧୦ 143602 روی 143599 egang 143596 ▁získ 143596 apä 143594 ▁17- 143592 ើប 143588 ▁distintos 143584 વવા 143579 ▁Зе 143577 sj 143571 ▁rakst 143567 ▁perjalanan 143563 ▁زنان 143563 คะ 143562 ▁இருந்து 143562 ▁кажа 143561 ▁nemen 143557 ژه 143555 ▁acabar 143554 ▁Ash 143547 ▁Госпо 143537 ▁карти 143534 ▁پري 143534 ▁banjur 143532 ▁بازدید 143530 ▁громад 143529 ▁nop 143528 ▁תש 143525 ▁kolm 143523 იან 143519 גש 143517 ▁wilayah 143510 ▁(14) 143507 ♪ 143504 ▁крити 143503 ▁175 143502 ▁здо 143499 ▁toko 143494 ▁kuya 143489 绝 143485 ▁vyb 143474 ▁phambili 143471 ▁ქალაქ 143469 ▁bago 143467 ▁Etter 143464 ▁niso 143462 ▁sareng 143462 ▁Alo 143454 ATION 143451 ▁trận 143451 aliśmy 143447 ▁panel 143442 apul 143441 ट्ट 143440 ukul 143438 ▁gaya 143436 empe 143430 ēšana 143422 ▁Afr 143422 هان 143419 련 143418 ጠና 143416 ▁порта 143414 ំព 143413 bid 143405 ▁kormány 143398 ▁غذ 143398 ▁hvilket 143393 ▁된 143392 All 143389 ਰਾਂ 143385 ▁feder 143385 hyr 143384 ಾಟ 143383 ▁kwakhe 143383 ▁Crist 143381 วัด 143377 ikhulu 143363 กิจ 143361 ▁kamay 143355 ▁الاس 143354 ື່ອງ 143352 ғо 143342 angnya 143340 odas 143331 ▁najbardziej 143331 ্যাম 143327 ▁bazı 143326 пей 143321 ▁λί 143319 MAN 143312 ▁jedn 143312 ▁لاہور 143311 ▁Xo 143306 ලිය 143304 fahren 143301 しまう 143301 ▁momentos 143295 ▁تعلق 143295 alap 143290 ▁محک 143289 દેશ 143288 ▁블 143284 ▁Pek 143274 ▁milj 143262 чете 143260 цем 143258 ▁европей 143253 ▁tote 143250 ▁Bou 143246 ▁инте 143232 ▁szabad 143225 ▁булы 143207 只能 143199 ▁rupa 143194 anggap 143193 ▁кварти 143192 ëse 143188 ▁Ring 143188 ezko 143187 ▁momentu 143187 左右 143187 ▁asturiana 143184 ▁Nederlands 143181 ▁ວິ 143172 oszt 143171 ेंगे 143164 積 143163 ▁баҫ 143155 ▁ڪريو 143147 ilmesi 143146 ▁tī 143136 ▁serve 143134 ▁വലിയ 143132 ▁produz 143121 ▁էջ 143119 ▁gute 143112 Li 143110 ▁mamy 143097 ▁تنهن 143090 ổi 143088 jenih 143084 살 143084 ▁pravi 143079 ပိုင္း 143078 uoja 143075 가지 143075 ▁půj 143074 avoir 143072 ▁Direk 143063 ▁регу 143058 ұр 143057 ▁varias 143056 ▁شاعر 143056 ogera 143052 alaan 143046 ▁orta 143046 ▁okup 143045 ▁Organis 143044 ▁dúas 143044 ▁режим 143043 jeta 143042 ▁לנ 143040 ▁საქართველოში 143033 ▁ខ្លួន 143031 ▁Turki 143028 στό 143024 ▁hry 143019 ouch 143015 ţiile 143009 ▁ქალ 143006 另外 143005 så 142993 ▁انواع 142991 зай 142987 ▁Acest 142981 されています 142981 dha 142980 เช่น 142979 ـــ 142976 ▁многие 142975 ▁concer 142974 ▁torna 142973 никам 142972 ▁Derfor 142972 ▁sarad 142968 ▁atlik 142962 ▁zom 142960 ▁amanani 142955 ▁koordin 142955 тү 142949 ▁dame 142947 тного 142945 ▁कस 142945 хів 142941 ▁पाल 142940 ▁lubong 142938 ▁Visa 142937 ▁ਸਭ 142937 কা 142936 lín 142928 ▁Thị 142926 ▁τρα 142924 adto 142918 ▁이유 142917 tira 142914 ▁współ 142907 ▁huquq 142904 ▁रंग 142896 ▁ବହୁ 142882 ရှ 142880 ត្តិ 142879 ▁calon 142876 ▁olmay 142874 herë 142873 10, 142868 sos 142867 ▁chiqar 142864 ▁ਦੁ 142863 ▁pelbagai 142856 áis 142855 ▁świet 142855 һенә 142853 結婚 142853 ٹے 142850 cima 142848 гәр 142845 ▁Anim 142838 masing 142837 /2014 142835 ▁aans 142835 oloji 142834 ▁зла 142834 極 142833 ▁eneste 142832 нець 142831 ▁nhật 142827 ინის 142818 БИ 142813 ▁uporablja 142807 ▁Rim 142798 ാരം 142797 yini 142793 ▁eel 142789 硬 142784 ▁кә 142782 ession 142781 ▁plă 142779 เพราะ 142774 सू 142771 დები 142767 ▁بما 142767 ഹം 142765 ▁бая 142763 غا 142759 ▁Ard 142758 zieć 142755 umsi 142754 ▁ଥ 142739 Fran 142734 ▁delles 142731 ometr 142730 трима 142730 ▁દેશ 142730 วิทย 142727 ▁Ple 142719 вто 142718 ▁Praha 142718 ▁заку 142710 ไซ 142709 ったり 142709 ▁끝 142698 ξι 142696 ▁ജി 142691 ▁fert 142684 тәрен 142683 டுக்க 142679 ▁punct 142678 ülő 142676 ▁recibir 142674 ▁зори 142669 ▁សូម 142662 ंदा 142655 vő 142654 ▁ataupun 142649 ▁telo 142646 ▁관리 142643 ▁azken 142642 kani 142640 ▁ниндәй 142640 ículo 142637 ▁கொண்டு 142633 ارهای 142630 ▁σύν 142628 ▁Центра 142622 เฉพาะ 142621 ▁сү 142621 ▁Oyun 142619 ▁શક 142617 ლებს 142616 ▁lectura 142615 กิน 142610 ▁adott 142599 ▁դաս 142599 ▁त्यामुळे 142599 ▁ray 142598 )、 142591 ▁اطلاع 142591 ▁vznik 142581 ▁vetë 142577 ▁good 142576 ▁उन्हें 142574 yah 142571 ▁depend 142571 ૩ 142569 čiu 142567 ▁Got 142565 ▁mennes 142565 ▁ذهن 142563 ▁گے۔ 142563 重要的 142563 ▁hawwe 142559 ▁мектеп 142558 ▁Nagy 142553 ▁2002. 142545 ▁Solo 142543 ▁находится 142541 ▁яр 142537 ▁সাথে 142530 악 142530 ▁VR 142513 来自 142513 ▁hí 142511 ▁ikan 142510 ▁Skr 142509 ▁ေဆ 142509 ▁আই 142507 nám 142503 ▁акту 142497 ▁உய 142496 olewa 142495 興 142488 ▁combin 142481 ▁පාර 142481 のような 142477 ▁fò 142476 ernet 142464 ▁pravo 142454 ullen 142452 ▁param 142452 ▁style 142429 rani 142427 ▁chồng 142426 ▁kích 142420 ▁Сла 142420 ുകൊ 142415 டிக்க 142407 ▁වසර 142407 saj 142404 ▁курс 142401 ▁укра 142398 ▁aca 142397 ▁अस्पताल 142395 ▁UR 142394 ▁राष्ट्रपति 142391 дали 142389 ▁creación 142385 ▁vòng 142385 ▁һаҡлау 142384 իպ 142378 ▁شريف 142374 ersen 142371 ▁நோ 142371 ▁прогно 142364 вань 142361 ▁കണ്ട 142356 ▁γίνει 142354 ervat 142345 ▁million 142339 ▁EV 142333 ▁үн 142332 ▁Tây 142328 ▁الأح 142328 ▁دینے 142327 ▁എല്ലാ 142326 ▁kain 142325 ▁hiszen 142322 ▁naud 142320 ▁mangsa 142319 iş 142318 権 142314 ▁паліты 142312 ▁لباس 142312 ▁govor 142309 ▁davon 142305 ሏ 142299 ствена 142298 ▁entend 142297 ▁1979 142290 ▁Labor 142290 ▁acolo 142290 είου 142286 bord 142279 ▁Network 142279 ნენ 142278 すれば 142277 最初 142276 ▁yardım 142272 ٹا 142269 allis 142267 ulte 142266 ▁સ્થ 142266 aston 142256 ▁ysgol 142255 voja 142252 čiti 142242 ▁закона 142241 ▁ignor 142239 ▁Kort 142233 nologi 142231 ▁उसे 142229 ליה 142228 ▁ေျပာ 142225 ▁takich 142224 ຮຽນ 142219 ▁Sund 142219 ▁ମହିଳା 142213 ▁magnet 142210 ▁russ 142210 ▁міся 142202 өөс 142201 ▁واضح 142197 れている 142192 ▁dostup 142190 nden 142187 ▁کون 142181 alue 142179 ▁Баш 142179 ▁maio 142177 ▁plani 142174 nių 142166 ▁bieten 142161 浮 142156 最新 142153 ▁Fle 142152 ▁àm 142152 ▁neće 142151 传统 142151 ствуют 142149 view 142147 ▁соответствии 142145 ▁ಭಾರತ 142139 ધા 142137 ▁дипло 142137 ▁കൂട്ട 142132 ▁bayi 142129 ▁بسیاری 142126 ▁պար 142125 ಂಡ್ 142121 ແຕ່ 142110 เก็บ 142109 ▁wisata 142109 ▁tager 142107 ЧИ 142105 ুক্ত 142105 ▁afirma 142105 ေနာ္ 142103 ภาษา 142093 isal 142092 ኤል 142092 ▁Spar 142086 шек 142085 ▁በኢትዮጵያ 142085 ▁Sarajevu 142082 امة 142078 ▁možnost 142073 ▁천 142054 ▁музыка 142050 ▁często 142048 ยุ 142047 uas 142044 ▁chạy 142041 qam 142040 ▁კო 142029 ျပန္ 142025 ▁பூ 142022 战略 142022 ▁pengalaman 142021 ativi 142019 FE 142014 ▁põhj 142011 アル 142006 ▁blu 142001 大会 142001 ▁voy 141996 ▁سات 141996 ដ្ឋាន 141994 ▁ajudar 141990 ▁demanda 141988 ▁һеҙ 141985 ▁megold 141977 ▁venta 141977 alb 141964 ▁апрель 141958 onz 141957 ▁Hàn 141956 ▁itilize 141955 ▁Petro 141953 чийн 141952 ▁محیط 141952 czas 141951 ▁innhold 141951 ▁ಮೊದಲ 141950 untos 141941 рово 141923 ▁NEWS 141923 ▁ਹਾਂ 141907 ▁Когда 141904 ▁असून 141898 ▁ски 141890 ▁Funda 141889 ▁tea 141889 ▁انتشار 141884 ▁nere 141874 ▁цэ 141872 ികൾ 141871 ykk 141870 ▁एउटा 141870 ▁өҫт 141868 ▁ngarud 141867 ▁ସୂଚନା 141860 اما 141859 ▁maging 141855 ▁sif 141855 INO 141849 ▁поча 141845 ▁okuy 141843 ▁potre 141843 ▁Esto 141841 ▁retir 141840 နဲ႕ 141839 ▁Kana 141830 usių 141829 ▁λά 141828 ▁jornada 141826 ົວ 141822 ▁seves 141821 esif 141815 ▁vond 141815 してください 141814 ▁მთავ 141810 ▁pouvoir 141806 сата 141802 ▁ҡыҙ 141800 ▁muuta 141795 പ്പാ 141792 厚 141791 ▁irak 141789 ▁következő 141782 ▁следва 141782 ▁Article 141780 ▁võimal 141780 順 141780 amuu 141774 েলা 141768 ▁उसके 141767 ateľov 141761 ầu 141760 ▁Sist 141758 ленных 141749 ▁Қо 141748 ▁կյան 141747 ფრ 141743 僕 141743 ▁bout 141740 стъп 141728 ▁Taip 141727 ▁enw 141723 ສ່ວນ 141720 alma 141712 ▁60- 141711 ▁keni 141708 латын 141707 ▁ಸೂ 141704 ▁môn 141703 حه 141701 ▁langit 141696 ÁR 141694 odne 141693 ficar 141690 ▁đáp 141689 धार 141687 ▁infantil 141683 ▁hyg 141681 ▁eby 141672 AMB 141671 ▁facile 141666 ▁обеспе 141659 ▁patri 141655 ▁primeru 141652 ▁skole 141651 ordon 141650 のある 141649 ▁væri 141643 ▁آقای 141639 ▁تصویر 141638 ▁kabel 141636 onis 141630 ▁հնարավոր 141627 ▁İran 141624 ▁jaoks 141620 ▁xanh 141618 ▁μεταξύ 141615 ajā 141612 ▁ကြ 141612 拥有 141608 ▁rende 141606 ▁Ask 141605 ▁Prí 141602 ʼ 141601 ▁25% 141600 folk 141592 angel 141587 ▁가격 141585 НЯ 141577 ▁Sana 141574 adat 141568 Kom 141565 ്യൂ 141561 ▁poolt 141557 olare 141556 ▁Todo 141551 ុច 141549 ंसाठी 141546 เห 141545 стай 141535 amanya 141531 ▁прису 141529 ▁böyle 141525 ิร์ 141524 ▁tulisan 141521 धिक 141519 ▁ava 141519 ▁gaur 141515 ▁minutter 141515 ▁coisa 141512 kým 141503 frica 141501 ▁hampir 141498 ▁កំពុង 141498 ▁Doma 141495 ല്ലോ 141491 რება 141490 あまり 141476 ▁ዋና 141469 ▁күҙ 141468 ▁അന 141465 ▁Preis 141462 போ 141458 ▁izvē 141458 ထိ 141457 ួល 141456 ▁නිය 141454 גון 141452 டா 141452 ▁στό 141446 ▁trochu 141445 ▁तह 141445 ▁تحقیق 141442 読 141429 ▁ngeunaan 141415 ldi 141412 штво 141404 idhinn 141402 ▁времето 141401 ყენ 141393 ▁Nueva 141391 erver 141383 ▁ពិ 141383 ▁plane 141379 шими 141377 ▁soft 141376 ▁पत्र 141375 აწ 141374 valg 141366 ▁البر 141356 alien 141354 ▁IC 141354 Ệ 141344 ▁natuurlijk 141344 icks 141340 θος 141340 ▁mówi 141338 univers 141335 ▁נת 141329 ாம் 141328 ▁역 141326 ▁تك 141325 ▁سامنے 141319 ▁материја 141316 ▁ځواکونو 141315 edom 141314 ▁Lí 141314 вари 141313 ▁obdob 141313 inska 141310 ▁prati 141306 нав 141297 的发展 141293 ▁වෙනවා 141277 ▁зараз 141273 ▁Dj 141270 嫌 141262 eton 141253 ືນ 141253 ▁tsh 141252 рез 141250 &# 141245 бло 141241 参与 141238 ანია 141232 ▁ስራ 141227 ▁તમારા 141226 дування 141223 ▁ሽ 141221 ▁дз 141219 ▁simpl 141207 ▁ажлын 141207 ▁Aks 141204 无法 141204 ▁yapt 141203 ▁Hoch 141200 ▁pond 141196 iaz 141182 /02 141180 ▁Kristo 141177 ▁catre 141176 وو 141171 ▁Español 141171 レン 141171 ▁ща 141166 ▁hòa 141165 ▁miejsca 141162 ▁View 141161 ▁terlihat 141161 ▁협 141161 alana 141159 જુ 141155 neo 141154 iaus 141152 ▁หาก 141151 Det 141148 ▁پھ 141144 omaan 141140 ▁кв 141128 Де 141116 ▁Premium 141116 ▁report 141109 ▁رسم 141109 ▁rəhbər 141108 ▁향 141107 ▁chef 141095 ▁ലി 141085 ▁ალ 141084 ▁Ана 141081 ▁الموا 141081 ▁menurut 141072 gus 141070 тыў 141068 ▁ښار 141068 iar 141060 ▁nằm 141052 ▁herri 141051 ファ 141050 指出 141043 ື້ອ 141041 ▁प्रे 141041 zg 141035 ítése 141035 อ่าน 141022 ëscht 141019 ನ್ಯ 141014 ▁senere 141012 ovor 141008 ▁pev 141006 ībai 140998 ▁karaa 140992 මයි 140991 abka 140989 ▁Dương 140988 ▁hisob 140987 ▁политика 140980 elei 140978 âld 140975 ▁новости 140969 ceu 140968 ▁★ 140965 taw 140964 ▁Opera 140964 ▁Però 140956 rios 140955 ▁carne 140951 ▁mawa 140951 ▁उच्च 140950 ▁सुरू 140949 ▁없다 140949 éf 140947 сю 140946 있는 140945 vide 140943 हित 140942 冰 140942 [3] 140941 ▁Information 140929 ंवर 140928 ▁รุ่น 140924 ▁stab 140921 ▁cientí 140917 āta 140914 ಿಯನ್ನು 140913 ▁remar 140913 暗 140913 ▁lạnh 140906 umbe 140904 眾 140904 ▁visus 140896 ▁ור 140894 ▁inlägg 140890 ▁Bru 140888 دای 140883 ▁faha 140874 čer 140867 ראש 140862 ▁menit 140861 ▁jaman 140857 ▁אונ 140857 ▁گرم 140856 全部 140853 jn 140852 ▁Roedd 140852 ▁واپس 140850 נוי 140844 യായിരുന്നു 140842 ▁ასე 140842 ▁დღე 140841 ığ 140840 ិយ 140839 âne 140838 ▁svim 140836 ▁esimerkiksi 140835 ¥ 140833 ▁пля 140822 าะ 140819 ▁Mong 140812 짜 140811 ▁Call 140807 ▁dever 140806 ▁proyecto 140797 limi 140791 ▁cursos 140788 ▁receb 140783 ▁כא 140783 κών 140782 ▁trái 140777 ▁wajen 140765 ▁ср 140758 ▁पुणे 140747 ▁געווארן 140737 ▁भे 140737 ▁අහ 140736 を選 140716 ▁leider 140715 deren 140713 ΕΣ 140712 ▁ನಂತರ 140712 වන්න 140711 ▁עק 140708 jega 140705 Ajuntament 140703 ▁ඇම 140702 ▁ಬದಲ 140701 ▁tą 140700 ▁isteach 140698 ▁Gest 140693 ▁DAR 140688 ኢ 140681 ▁diversas 140680 uvre 140679 ▁Vern 140677 ▁pelajar 140669 ▁نار 140668 ▁براي 140666 ▁इतर 140663 iments 140652 ▁சொ 140651 चल 140647 징 140641 ▁huduma 140640 ▁Kli 140633 ▁Ông 140631 安心 140628 iega 140626 ▁පිළි 140626 ▁прика 140622 side 140621 利益 140617 ▁РЕ 140614 веро 140607 ▁چارو 140605 ବୁ 140604 ▁Kec 140600 แม 140598 ხმ 140590 ▁обы 140590 СР 140589 ▁եղել 140587 ▁kənd 140583 blad 140580 embo 140580 ▁جھ 140573 ovaní 140569 ▁plastik 140567 ▁scris 140566 ▁ունի 140566 ၍ 140561 ▁דו 140561 ▁Elec 140559 ▁resist 140557 ▁επιχει 140554 icare 140551 ▁osobných 140549 ▁โปร 140546 ▁ചെയ് 140543 ▁одр 140541 กุ 140537 ▁VA 140533 ▁nomb 140530 κίνη 140529 ንት 140527 ▁Profes 140527 IRA 140521 ▁10.000 140519 通常 140517 ▁Dana 140509 ▁अर् 140504 ▁नगरपालिका 140504 ▁хор 140501 alung 140495 ანდ 140490 ေဆ 140485 ▁nip 140467 ▁laget 140466 ▁факти 140465 дія 140461 ▁hút 140457 ▁നടത്ത 140452 ▁Isk 140451 ▁הרו 140449 ▁الحر 140448 ikum 140447 ització 140437 ▁pdf 140435 ▁ଦଳ 140432 ▁doprav 140428 ▁Gia 140427 hotel 140426 ସ୍ଥ 140423 ələrində 140420 甲 140414 ▁된다 140405 :59 140399 ▁droit 140395 ▁шля 140389 ▁Sic 140387 ▁kurz 140377 ▁Spi 140376 ▁juda 140366 ▁Market 140364 ▁ڳوٺ 140362 だろう 140356 well 140354 ▁vastaan 140354 ▁đông 140349 ▁معرفی 140348 ▁diwar 140346 ▁Nett 140343 rique 140340 ындай 140338 ▁Fernando 140331 оп 140330 ▁öl 140319 ▁ету 140304 ▁околу 140302 ▁autobus 140299 ▁našich 140298 ▁qualidade 140285 ▁richi 140281 ▁חדש 140279 ▁ndlela 140275 ▁vài 140272 ▁Tür 140270 ▁między 140270 ▁ansi 140259 ▁Wik 140258 ▁위치 140258 arrak 140251 jący 140249 ▁haq 140248 ▁dokon 140246 ▁noastre 140244 ▁fosse 140241 ▁뒤 140241 ▁Jūsų 140235 祝 140235 ඥ 140234 มอง 140233 ▁mềm 140232 ▁wed 140227 ‟ 140220 *** 140212 ▁Baby 140203 ▁۱۰ 140193 讯 140193 ▁meus 140192 ▁Spring 140191 ▁сра 140183 ▁nüüd 140179 mise 140178 anni 140177 ▁Naglangkob 140175 1: 140173 emain 140173 ▁Multi 140173 LAR 140170 ▁másik 140170 喝 140168 ሪዎች 140166 ▁prijat 140166 ▁تیر 140166 ▁wena 140163 ▁England 140158 ▁ווע 140157 ▁وضعیت 140151 ▁24- 140145 ▁ogrom 140145 ▁sambil 140143 ▁poži 140138 пля 140130 ▁budaya 140127 unuz 140114 ▁फोन 140114 ▁ব্যবহার 140114 clus 140111 ზად 140105 ชุด 140103 fie 140101 ▁plaça 140098 っと 140097 ▁nzima 140092 ▁Bü 140091 ▁réalis 140086 дум 140081 juan 140076 ▁Nal 140074 ▁Xasan 140074 azioa 140072 еү 140063 ▁Tart 140063 ruck 140061 Ê 140058 ▁મેળ 140056 χος 140054 Po 140052 ತ್ವ 140046 坚持 140040 ▁Erk 140038 ējās 140036 ▁Gàidhealtachd 140035 ▁XVII 140035 рано 140033 年前 140033 ▁където 140029 លេខ 140024 ▁Manager 140024 razione 140020 ləş 140013 юз 140013 ▁kritik 140011 ▁oNgcwele 140011 ጁ 140009 ▁عنه 140007 ▁tindakan 140006 ulela 140001 ▁Besuch 140001 onti 139990 ▁skoro 139989 ▁deği 139988 ▁барлыҡҡа 139988 ète 139987 ▁języ 139974 ▁disponibil 139971 ▁bilm 139970 izacion 139969 શી 139968 jada 139967 ასთან 139965 கின்ற 139962 ಧಿ 139961 ▁austr 139961 ▁prihod 139958 jeren 139955 ▁жүзеге 139955 ሉ፡፡ 139954 輕 139954 คืน 139948 маны 139932 ▁Mest 139929 ▁هنا 139929 ▁ٿر 139924 学院 139924 นาย 139918 unywa 139916 فض 139913 ▁ڏنو 139913 şın 139887 ▁टीम 139882 ಪಡ 139881 ▁incom 139881 ▁amigo 139879 φαλ 139874 ▁chini 139870 ▁χώρα 139870 測 139867 núť 139866 ▁kia 139865 جية 139859 ຕາມ 139856 ▁Lac 139853 ▁хэв 139851 ▁suht 139849 ▁ඒත් 139846 ▁името 139843 ▁denken 139837 ▁međunarod 139837 态 139837 δικ 139835 ▁Selv 139833 ▁انتظار 139830 áč 139829 ▁δη 139828 ▁नग 139822 ▁budete 139820 ▁laser 139817 ▁Kosova 139810 ▁Blu 139809 ▁فت 139808 igheden 139804 Kristu 139795 ▁ਹੋਵੇ 139795 ▁Wan 139794 担 139788 ▁hik 139786 ▁Στην 139784 秋 139784 unot 139783 ▁nettet 139776 ▁avèk 139774 ▁importancia 139773 ▁blod 139772 でしょうか 139769 ▁최근 139763 达到 139759 ▁ହେ 139758 ▁карта 139756 ▁између 139754 ▁എന്നിവ 139754 ▁4,5 139753 ▁만들 139753 ▁Yar 139740 ▁Guð 139724 ▁annen 139722 ▁ଟି 139720 ▁penc 139719 基金 139717 ▁kapag 139713 ▁zainteres 139713 мыс 139712 ▁vienu 139712 เพิ่มเติม 139709 ▁tầng 139707 ▁lakukan 139704 ▁internationale 139700 ▁comprom 139698 மல் 139693 陆 139693 യാന് 139689 ▁Linn 139683 ▁kare 139683 ▁možnosti 139682 ▁acestea 139679 ▁Toyota 139677 oksen 139671 ▁පුවත් 139670 ▁AMO 139666 chuu 139659 ▁lais 139658 laan 139644 ▁સામે 139639 ▁Sg 139632 ЛЬ 139628 ▁មនុស្ស 139627 ▁дета 139622 ▁geng 139621 onym 139619 uned 139619 лета 139616 ▁परीक्षा 139612 стың 139606 ▁DS 139599 ▁آيو 139595 ▁anatin 139593 ▁کېږي 139593 ▁розпо 139592 ▁eten 139591 gian 139589 ▁ଫେ 139589 ▁ವ್ಯ 139588 ▁դարձ 139587 šení 139586 ಿಂಗ್ 139586 ▁інші 139585 oratra 139581 vate 139577 ▁οπο 139577 ▁مشاهده 139573 ▁මාධ් 139562 ուղ 139556 ียร์ 139545 ▁Prezidenti 139545 נטרנט 139543 стры 139542 czego 139539 ▁berjalan 139538 ජන 139537 dé 139536 ▁хочу 139519 adamente 139512 ▁contract 139504 ▁breve 139500 ▁ענ 139499 ▁asko 139492 ▁ଯାଇ 139491 ▁internetu 139488 をつ 139488 ▁înc 139486 ▁findet 139481 ▁smak 139476 ▁окре 139476 ▁neo 139467 ▁سيد 139466 ▁pref 139464 ▁хай 139456 ▁deber 139452 智能 139447 dert 139444 ▁سچ 139442 adra 139437 itātes 139431 장을 139427 asikan 139422 ▁wur 139419 皇 139412 তির 139411 ▁CN 139401 สะ 139394 ASH 139384 ▁kabul 139383 უფ 139379 ▁وکړ 139378 ▁plata 139367 ▁Μέ 139366 āda 139364 ائم 139357 ▁gehad 139357 ▁buen 139356 ▁маши 139355 ▁пото 139348 ▁vett 139344 ▁nomin 139343 قص 139340 ▁پشت 139338 untza 139335 ▁åb 139334 调查 139334 ▁කරලා 139333 wur 139329 ēma 139328 ▁prizn 139327 дил 139326 ▁wyp 139323 ▁KAY 139321 ዝብ 139320 isim 139318 ▁Jums 139314 ▁teknolog 139314 ▁trouve 139309 ▁eigenen 139306 ▁සේව 139304 ▁Ngoài 139303 ▁gange 139300 ▁পা 139298 कृ 139285 ▁Popul 139281 ▁Arbeits 139270 ▁alde 139270 nąć 139266 ▁2018- 139265 andon 139261 áh 139257 ▁Cultural 139254 ريل 139253 ▁ਰਿ 139253 urit 139252 ವರ್ 139252 яна 139250 舞 139249 ▁ഒന്ന 139244 érie 139241 ▁връ 139241 ▁факт 139241 ▁இருக்கும் 139235 ▁atbild 139230 isee 139229 ▁estan 139229 ▁syk 139214 ▁został 139214 จาร 139202 endom 139201 njih 139197 ▁bilen 139194 ▁безопасности 139192 ▁نج 139189 ▁ಕಾಂಗ್ರೆಸ್ 139183 ിക്കുന്നത് 139179 ▁anay 139179 ▁Hau 139178 ▁ngwa 139178 ▁Polic 139176 ?» 139174 ňuje 139173 ▁souvent 139168 ▁rehabil 139167 ▁tł 139157 jór 139153 ▁síð 139150 ▁تھی۔ 139150 ▁разбира 139145 ijā 139143 そして 139142 ▁رسانه 139137 → 139134 ▁offer 139134 ▁قومی 139133 ▁الجمعة 139128 ▁slike 139124 ▁ඇතු 139124 ▁miễn 139118 ▁blandt 139115 ▁asia 139108 orto 139104 ▁Baj 139102 ▁گرفتار 139098 ▁abord 139093 luž 139081 根本 139079 ▁Honda 139078 jerne 139077 ormente 139077 ▁lly 139076 ▁Hospital 139071 ▁burada 139070 ▁maana 139065 сал 139054 ▁неиз 139053 тивна 139042 ▁ποι 139042 ▁Құ 139040 ▁inicial 139039 ▁Manila 139030 ▁Moto 139015 ատր 139011 igheder 139006 GP 139005 ▁מתח 139004 ▁يش 139002 ırım 138994 pur 138993 γρα 138992 ньої 138991 ▁opoz 138991 ▁olmayan 138989 ▁valores 138989 ▁Segundo 138987 ▁2.5 138983 อย่างไร 138982 halb 138980 ▁artista 138979 ▁جنرل 138958 หาก 138957 ▁máa 138955 ▁varm 138953 ▁السعودية 138944 ORA 138943 ▁nál 138943 ▁hemos 138942 ▁yuz 138939 ▁ਗੱਲ 138939 ▁अशा 138938 幅 138938 ณ์ 138937 λώ 138936 ▁spal 138933 īz 138932 ුණා 138929 ਵਾਈ 138922 тиме 138919 อส 138912 ▁ایسے 138912 なる 138902 ▁θεω 138901 είται 138900 ▁veic 138896 ុះ 138895 ▁trời 138891 бору 138890 ▁الخميس 138888 รอบ 138884 ▁mande 138882 là 138879 ▁Видео 138877 ▁доказа 138875 сме 138869 atures 138863 ▁ٿئي 138860 ▁delo 138857 ▁लिया 138857 ▁Sht 138853 ▁entrega 138852 説明 138851 ▁இல 138849 ▁Pran 138842 ▁plaas 138841 人才 138841 ▁yakin 138839 єте 138829 ▁fisi 138829 ▁egészség 138825 elje 138821 úin 138820 ▁informácie 138820 ▁маг 138817 ▁koc 138814 рене 138813 權 138807 ുമ്പോള് 138806 ▁وكان 138804 umur 138802 ▁Այ 138801 ▁Neste 138799 FL 138798 ▁екен 138797 ودة 138796 āko 138795 kuntza 138786 [2]. 138784 ồ 138779 ▁марта 138779 skapet 138775 ព្រះ 138773 ▁wonten 138771 ▁συνο 138771 ▁nafasi 138768 ▁hili 138766 ▁કાર 138765 étr 138764 ਕਾਂ 138760 ▁Abd 138760 ▁kepala 138742 ▁výro 138740 ▁имаат 138739 ត្រូវ 138727 ▁Kaf 138727 ▁ustvar 138727 ▁هزینه 138723 ▁1944 138722 ელის 138720 ▁¶ 138720 μον 138717 ▁žád 138711 lẹ 138709 ▁учили 138699 ▁ณ 138697 ▁fih 138692 ▁Ito 138688 ▁jes 138687 ▁negli 138687 सहित 138675 ▁Guru 138674 ▁Украины 138672 ტე 138671 หญิง 138668 ▁glæ 138668 קטי 138666 ▁ಹೆಚ್ಚು 138666 რით 138659 ▁почему 138639 ▁маршру 138638 tut 138637 سط 138633 េក 138633 ituksen 138627 ▁masz 138627 べき 138626 ▁şəhər 138625 atique 138615 ▁Yor 138611 oriu 138606 ▁Bai 138604 +1 138602 ▁forstå 138602 ▁Dum 138601 شتر 138597 льную 138595 ▁אבי 138592 gah 138589 דיש 138584 に関 138583 ▁giác 138579 ▁všet 138579 uraa 138577 ▁percaya 138576 ុយ 138575 िएका 138571 lân 138561 ¡ 138560 ▁trois 138555 izlik 138552 ▁Raya 138552 ▁ekspert 138550 ▁þarf 138549 alık 138546 خذ 138546 اج 138543 pozn 138532 ▁[2] 138531 mang 138528 8- 138527 ▁presti 138523 vän 138520 РУ 138520 ખી 138519 ▁স্ট 138519 ▁cose 138514 ОВИ 138504 ХА 138502 ▁xeneral 138498 стів 138491 omy 138488 keiten 138487 ▁hoch 138483 iOS 138482 ▁давно 138482 ▁(„ 138481 候 138481 ▁ajuns 138473 pic 138470 дийг 138470 ்த்த 138470 ▁эн 138466 hrú 138464 èy 138464 ▁lla 138463 ▁වෙනුවෙන් 138462 ▁bakho 138458 年度 138454 antos 138452 ूप 138452 fiz 138451 utom 138450 ▁Satu 138450 ▁فون 138448 ▁bero 138446 ▁oziroma 138431 ▁للا 138430 otor 138429 ▁Tod 138426 цер 138425 ▁सू 138425 ▁tapaht 138419 ▁laz 138415 पति 138413 ኝነት 138410 ▁íslensk 138404 ▁προσωπ 138402 λούν 138399 ▁חז 138399 ستم 138398 ളിൽ 138397 edka 138396 ▁شهرستان 138395 ▁dhidi 138389 ▁spiller 138384 чэн 138383 әп 138379 ▁hiểm 138378 անալ 138368 േഖ 138368 ▁тәҡдим 138364 ▁kalend 138363 ▁неколку 138363 ▁وخ 138362 こんな 138360 artzen 138350 ուրդ 138349 ▁이어 138347 ▁giống 138343 玉 138342 empo 138339 ▁սիր 138339 ន៍ 138338 ▁Sib 138338 ື້ 138334 ▁dvs 138334 ▁করার 138334 ▁ਥ 138331 ▁помощ 138318 ▁ମି 138317 ▁amaz 138313 ▁використову 138311 ▁Sams 138310 去年 138305 ▁Russia 138298 ▁शहर 138296 ▁Ngày 138294 ตลาด 138293 ▁scho 138283 aces 138280 ▁win 138277 orias 138269 וכן 138269 ▁しかし 138268 երով 138267 ▁업 138263 ▁दुनिया 138257 ▁ferie 138254 र्क 138244 ▁коју 138240 ▁распа 138240 ▁וגם 138240 hioù 138235 ေတာ 138235 ▁بده 138230 ▁iep 138228 ▁isan 138226 util 138219 umele 138212 ▁Kaya 138210 ▁stránka 138208 ▁škole 138206 ▁1917 138204 ▁despois 138203 ▁dienas 138203 ዲያ 138202 ▁parla 138202 ▁izinhlobo 138199 ▁සිටින 138198 .! 138195 koon 138193 ক্ষে 138193 ▁kosa 138193 ▁pirk 138187 ▁התק 138186 ▁acuerdo 138181 ว่าจะ 138180 ▁پارٹی 138175 ▁lanç 138172 학교 138172 ▁egyéb 138171 ▁Yaz 138170 ▁popula 138168 ▁kréien 138166 HR 138164 坚 138161 ▁ван 138160 ೀಯ 138157 ▁najm 138149 ▁Moldova 138147 ору 138140 ဏ္ 138140 ებდა 138140 ▁τό 138137 ▁kemur 138129 ▁грамад 138129 ▁dispar 138128 ▁tarp 138128 מור 138126 ácii 138125 átt 138117 ▁Ստ 138117 ▁nivo 138114 ▁шиг 138114 FT 138113 weer 138113 шат 138108 ▁passi 138106 -22 138089 成本 138089 ▁boshl 138087 ▁እነ 138086 ▁šis 138066 ▁особенно 138062 ▁moot 138061 ▁Fast 138057 ▁Kost 138057 ▁ایسا 138052 傷 138052 ▁cí 138051 rž 138048 ▁existen 138046 ▁Rīgas 138043 ▁vietā 138039 ▁غرب 138030 ▁ମନ 138029 ▁locale 138028 ▁नई 138027 ▁персо 138022 ▁191 138019 geg 138011 ួរ 138008 ર્ણ 138006 ▁Україна 138005 yong 138003 ▁maneira 137997 ▁lij 137996 èrt 137989 රේ 137985 ▁אותה 137985 ▁نتیجه 137979 icab 137978 ▁danes 137978 ▁тик 137978 持续 137978 elda 137977 รัฐ 137976 ▁सेक्स 137973 ▁ହେଲା 137972 أت 137965 empre 137958 ▁ነበር። 137934 ▁waf 137933 ección 137932 olari 137932 资金 137930 atorii 137927 ▁produse 137919 čina 137917 िति 137916 emist 137914 ▁шар 137913 ようです 137913 ຊາດ 137911 ▁Pala 137911 ▁اپنا 137908 ▁പോലെ 137902 ▁אנשים 137900 Lo 137895 աջ 137893 ▁бет 137890 rige 137887 ▁musim 137887 ▁woon 137883 ▁образ 137882 ndu 137871 ому 137864 itum 137862 ▁relax 137861 ደው 137858 лений 137855 ல்கள் 137852 ▁saad 137850 ರ್ಯ 137849 ▁40- 137849 ▁hatua 137849 ighean 137847 ▁برنامج 137847 ikaa 137846 ▁ಹೊಂದ 137841 ▁знаю 137835 ಸ್ಟ್ 137829 ▁elever 137829 ELA 137824 sidlakang 137819 ▁gov 137819 ▁mejores 137818 ▁რას 137818 ▁կազմ 137815 ▁सर्वा 137812 ▁ਬਣਾ 137810 2018. 137805 ▁solidar 137803 贵 137801 atat 137799 ▁мае 137794 ▁bash 137791 ▁ukur 137791 бина 137790 ▁zes 137784 ढ़ 137782 tavasti 137778 inja 137776 ម្ល 137776 機能 137775 ▁kompan 137772 zony 137768 ▁watan 137767 ▁millioner 137758 ಲಿದೆ 137751 ല്ലാം 137750 ▁जल 137749 ▁kert 137747 ▁совсем 137746 带来 137742 эхүү 137739 ▁fény 137736 ാൽ 137731 ▁حادث 137713 恐 137713 tager 137708 itati 137707 жих 137707 ▁kraf 137706 ▁هوندو 137706 库 137699 AKO 137697 νου 137694 ▁LI 137693 mula 137685 ▁сұ 137681 ▁насо 137679 ▁2004. 137676 ▁Winter 137673 [2][3] 137672 ▁đóng 137672 ▁dhèanamh 137671 μαστε 137669 告诉 137668 ämt 137666 ▁чле 137663 ases 137654 ▁KI 137651 ▁허 137650 ငံ 137648 ▁09. 137644 ▁offentlig 137642 apte 137640 ጆ 137640 ▁mees 137640 ▁дер 137638 ▁Rena 137635 θυ 137634 ໂດຍ 137633 ▁شعب 137628 ლებში 137627 ▁отка 137627 ▁Alm 137624 ▁uczest 137623 ▁pru 137621 ▁ghe 137619 였다 137619 HCM 137618 ческа 137608 anca 137604 ▁ԱՄՆ 137604 ▁អត្ថបទ 137604 head 137603 ▁dizze 137587 ▁практически 137585 erty 137573 гада 137573 כער 137573 મત 137572 باب 137563 ▁kyk 137561 ▁Pierre 137560 раат 137556 ▁bost 137556 odno 137554 ▁niile 137554 पर् 137547 8] 137546 렌 137541 kita 137536 ▁laang 137535 竞争 137535 ங்களில் 137529 ▁verða 137528 παν 137527 ▁sær 137524 ▁आयोग 137524 ▁kona 137522 ▁Hoàng 137513 ▁année 137511 ▁เซ 137505 ▁contoh 137498 teve 137495 ▁кола 137495 व्य 137489 泰 137488 経験 137484 ▁ต้อง 137482 вното 137481 ▁weit 137480 ▁දරුව 137479 oln 137461 казва 137459 ęż 137456 ▁программы 137449 ▁Cruz 137445 υμα 137443 ffent 137439 ezia 137438 ԻՆ 137434 mati 137428 ęci 137428 ▁санк 137426 ▁Sveriges 137424 ▁الأخ 137423 ▁yaxshi 137422 ▁نيوز 137421 Italia 137414 isie 137413 ▁XI 137402 ▁zun 137394 ▁야 137388 ົ້ນ 137387 AGA 137385 ▁Հայաստանում 137384 өлө 137381 rh 137379 ▁тэд 137373 ▁मुला 137373 廣 137365 ▁вважа 137361 ációs 137357 ▁ಶಿ 137357 न्ड 137354 ▁ਪੀ 137350 ▁uli 137348 ▁لب 137345 ML 137342 ழை 137334 ▁exec 137333 દ્ 137331 ▁कभी 137320 tación 137319 ▁Sports 137317 ▁ավ 137317 ▁ڪندو 137314 ▁hậu 137299 ennis 137298 ▁byr 137297 ▁логи 137294 ▁୬ 137289 ▁ខាង 137287 ▁kurios 137286 ostas 137284 ▁الثلاثاء 137280 ▁Luxembourg 137279 ▁արդեն 137279 ▁olsa 137277 ▁102 137275 ▁anyag 137275 Jehova 137273 戶 137272 UTC 137268 ▁obja 137266 ▁ஈ 137265 ▁tanda 137262 ▁оқушы 137260 ▁स्वत 137259 ましょう 137257 ▁හැකිය 137251 ▁пача 137250 اهد 137249 ▁מידע 137248 ▁ehd 137237 ynedd 137230 szego 137227 warz 137225 ୋଜ 137218 ▁revol 137216 ▁VIP 137207 ▁years 137205 ▁nemlig 137199 ▁сығары 137197 ▁ayam 137195 ziv 137193 ▁ተወ 137189 ▁дамыту 137186 vila 137182 ▁وٺ 137180 ▁marad 137178 ▁Tentang 137165 jeng 137164 elio 137161 ▁eie 137159 ıla 137157 리스 137155 ▁vare 137150 ▁توګه 137150 onha 137148 ▁آور 137130 álu 137127 ▁Бри 137126 ▁veid 137111 ▁olid 137107 ▁kohd 137106 ▁zel 137104 ▁száz 137097 ▁każdy 137094 จัดการ 137092 ▁izve 137091 ▁maɓɓe 137091 ▁фрон 137091 ▁відо 137083 ,,, 137080 dığı 137077 ▁destek 137072 ▁անդամ 137072 ੍ਰਿ 137068 лығына 137066 ▁документ 137061 amna 137059 ▁لدي 137059 Kr 137058 ラー 137058 事が 137057 յանն 137056 ▁موت 137052 ajtur 137049 ▁undan 137046 ▁diplomat 137044 ▁මහා 137040 પુ 137039 ▁123 137037 ▁उनको 137037 ▁ਰੱਖ 137037 地域 137037 igaba 137036 quin 137028 ▁торо 137028 ▁первый 137020 ագիր 137018 ्यता 137011 ▁Kann 137011 ುತ್ತಿದ್ದ 137009 ▁könnte 137002 ▁18:00 137001 ▁completamente 137001 ▁stronie 136999 ▁پروگرام 136994 ▁Sloveniji 136993 ▁starfs 136991 ▁правильно 136991 ▁confi 136988 ंज 136986 ▁بچوں 136978 ▁Ndër 136977 ▁ವಿಶ್ವ 136977 ▁ගේ 136977 ▁தின 136971 ვეს 136970 ▁traball 136968 ▁pagrind 136966 iris 136964 ▁දෙක 136964 乡 136964 /2013 136962 чок 136961 რუნ 136961 ▁Michel 136961 ▁غلام 136961 ▁ເສ 136958 ▁должна 136954 ASS 136951 isma 136942 ▁Кор 136942 ▁phút 136938 ونا 136935 ọta 136933 ▁соціаль 136933 ▁เปิด 136933 pụ 136930 layan 136928 ▁efici 136925 ង្ 136922 ▁sizin 136920 ▁uç 136920 രോ 136914 ▁Iwwer 136913 ▁septembra 136913 ▁Địa 136902 urut 136901 ▁upra 136894 ▁бод 136888 ▁sky 136881 ▁פרטי 136877 ▁termos 136875 дең 136873 тото 136870 ‭ 136868 ▁lej 136868 ▁жит 136868 ▁žmonių 136867 ▁ciò 136866 ▁beide 136864 vand 136863 ojmë 136861 ▁постоянно 136857 قى 136852 ▁باد 136851 ienti 136850 ▁आली 136850 ▁zemlje 136842 ORD 136840 льта 136836 rı 136835 ▁Shu 136833 ▁иск 136833 لاب 136830 átu 136829 عام 136829 arja 136827 andid 136826 ▁ولم 136826 大量 136824 दु 136818 หาร 136818 éhez 136817 ▁111 136817 ▁මෙහෙ 136815 uder 136810 ▁بلکہ 136806 ▁ipak 136802 许多 136792 互联网 136788 ustel 136784 ▁Pand 136782 ▁bambini 136767 nict 136766 ▁ചേ 136763 ▁문화 136763 ▁Kanada 136757 жного 136756 ຫມາຍ 136756 ▁siapa 136752 níci 136750 ▁Jual 136739 ▁୨୦୧ 136737 压 136737 æm 136736 াত্র 136736 ▁fər 136736 рого 136733 ▁καλύτε 136730 ▁begge 136723 ▁Ey 136719 kių 136716 ▁başladı 136712 ariamente 136711 ▁marcha 136711 ▁აზ 136711 ▁medal 136705 obec 136704 ▁ситуации 136703 ▁अति 136701 勤 136697 ▁cw 136691 ayaasha 136688 ହାର 136685 ነሳ 136684 ▁পড় 136675 ิ้ง 136674 ▁paper 136673 enste 136670 qish 136668 ▁нужда 136667 ؤں 136651 анта 136648 ▁coisas 136641 ysi 136640 ▁සෙ 136632 ▁ელ 136630 íg 136629 ङ्ग 136614 ▁Сле 136614 ▁бірі 136613 ▁ह्या 136611 houd 136610 xer 136610 ▁органі 136608 ▁пасля 136607 ваются 136606 ▁політи 136605 ▁jafn 136604 ▁fí 136602 öng 136597 ▁کسان 136595 ▁Provincia 136593 ▁berhasil 136593 ▁lur 136592 ов 136590 ▁sprawie 136580 ▁Біз 136573 ▁аш 136572 ▁gC 136568 ivyo 136564 ▁Hart 136561 Bar 136560 ▁катего 136557 ▁һөҙөмтә 136557 ▁θέμα 136555 ▁2017- 136552 心理 136552 ▁sən 136551 状況 136551 ▁Хар 136548 мақ 136546 ▁olet 136545 ▁corpor 136544 ສັດ 136537 ▁ezen 136537 ▁क्ष 136537 iday 136534 ▁köt 136534 ▁cég 136533 ▁ей 136533 ▁Thái 136532 ▁säker 136530 ▁الثاني 136525 ▁inggih 136524 ▁gilt 136517 ▁сарад 136517 थि 136512 estaan 136509 лед 136504 ▁suos 136504 ▁رای 136495 ▁ucz 136493 ▁legjobb 136489 훈 136488 leche 136476 本当に 136476 ැති 136474 וכל 136456 ງການ 136455 卫 136449 ပါတယ်။ 136441 tory 136440 ल्ला 136434 AMENT 136416 стері 136411 ▁ឆ 136409 ▁mpya 136408 ▁Львів 136408 ▁motho 136403 ▁مجموعه 136400 ▁druh 136397 ▁Dịch 136396 找到 136395 ளில் 136391 ួស 136386 lanmış 136384 ▁recherche 136381 ▁रहेका 136379 ogia 136372 ▁superi 136372 ▁работать 136357 ▁184 136351 ęg 136344 ▁surtout 136337 uhle 136336 ▁partecip 136332 ▁108 136330 ്ല 136327 ▁Allt 136327 ▁ತನ್ನ 136327 ตรา 136326 ▁طالب 136325 yka 136324 ▁сторін 136323 30, 136322 ▁όσο 136320 ▁mysl 136319 iley 136311 भी 136310 ▁dator 136308 Խ 136307 នៃ 136305 ▁Lahatsoratra 136303 NU 136302 вів 136299 ▁υπάρχουν 136295 urka 136292 ▁มัน 136289 ñar 136287 ▁остан 136287 ▁Và 136285 ussion 136273 ījumu 136267 ▁yakın 136263 ▁جنوب 136261 Do 136259 ▁біль 136256 inki 136254 ▁sicer 136253 зра 136248 eskus 136245 ▁سرب 136243 ಗೊಂಡ 136241 特别 136239 ▁gene 136229 ებმა 136220 ▁ଜାଣ 136219 ▁ቦታ 136217 ▁iest 136216 ▁Parte 136215 ▁Mens 136214 姿 136213 ▁필 136212 verd 136211 альные 136211 ▁Mount 136208 บอก 136207 ▁egyes 136205 ▁ਪੱ 136205 ▁Κο 136199 ▁эдийн 136199 ▁шийдвэр 136191 ▁Mary 136189 သြ 136186 ▁mỹ 136183 ▁mensa 136179 ▁참 136179 ừa 136175 ▁પો 136174 llä 136170 ▁εκα 136170 tero 136169 ▁участь 136168 mál 136167 ược 136165 援 136162 łod 136159 انات 136159 яза 136157 ▁loj 136157 ียม 136156 ພັກ 136144 11] 136143 ▁orqan 136143 ▁kalpasan 136140 ▁nikdy 136140 ▁етәксе 136138 kong 136137 دى 136135 ▁вместе 136135 ▁රධාන 136134 ▁educación 136133 ▁podp 136133 ▁însă 136133 enses 136131 ▁vpl 136130 hind 136126 정을 136124 annten 136122 ▁diet 136111 ▁sava 136111 ▁Шулай 136109 Os 136107 สาม 136106 ▁strada 136106 irib 136103 дома 136102 ▁erste 136102 ▁એટલે 136098 ▁وٺي 136095 ▁ними 136092 ▁Bund 136088 ذر 136079 ulás 136074 hält 136073 ▁hoor 136073 )( 136069 ▁رمضان 136062 ếu 136056 ▁ampak 136053 ▁uri 136042 પે 136037 ▁dieną 136035 ▁ବିଜ 136032 ូវ 136028 føl 136023 ▁месец 136023 ▁тул 136015 ▁second 136013 冬 136013 ▁Enter 136012 վար 136008 ▁котором 136008 ▁ທີ 135996 ወጣ 135995 ▁keputusan 135995 ▁زور 135990 ଯାଇ 135982 erah 135980 ▁ยัง 135980 ▁tehnolo 135978 ▁бой 135978 izado 135976 Foto 135974 ▁izinhl 135974 Ме 135973 ▁pouze 135967 ალურ 135961 chia 135954 還是 135953 ▁갖 135947 ▁추가 135946 ▁이후 135944 ▁niining 135935 ▁jana 135925 ▁iph 135924 ▁− 135921 ▁qaz 135919 ▁زه 135919 xis 135918 ちゃ 135912 ësinë 135911 ▁rambut 135910 ▁cca 135908 ▁egym 135908 ▁Nadal 135906 ▁chút 135905 ▁Russ 135897 ▁suu 135893 ▁активности 135892 ▁škola 135891 ▁العلم 135888 聽 135888 ための 135882 itada 135880 িউ 135871 áním 135857 allinen 135856 ▁uygul 135855 ▁costat 135854 ▁Põ 135853 ▁ຟ 135853 ▁መረጃ 135852 ▁gras 135841 ▁თამაშ 135839 quement 135838 ▁ningún 135824 ratti 135823 assan 135821 вага 135821 ခ်ဳပ္ 135821 стам 135809 ▁leas 135809 ▁таби 135789 แค่ 135778 ▁brī 135777 ▁педаго 135776 ▁Norske 135772 ▁ราย 135771 ▁nümayənd 135770 ▁cac 135768 ▁todella 135768 :55 135767 ▁Dere 135766 ▁пропа 135765 wig 135760 ▁enää 135760 ▁æt 135760 ▁experien 135751 φι 135748 ära 135739 òra 135739 ▁ଅନ୍ୟ 135739 ▁travers 135737 тарына 135736 ட்டை 135732 ▁fundament 135729 ▁Hải 135727 ▁unas 135726 ▁میرے 135721 ündə 135716 КЕ 135714 ƴ 135702 ▁נש 135702 rych 135693 ▁müddət 135691 ▁ٹیم 135689 ▁aktivnosti 135688 ოგ 135686 ▁εξε 135686 ▁заги 135686 ▁назна 135676 έρα 135674 ▁ուս 135673 ▁pust 135672 ▁зали 135667 ▁мире 135663 ▁podatkov 135662 ▁ଦେ 135659 irse 135658 ▁Constitu 135658 איז 135657 ▁मज 135655 ▁داستان 135648 вља 135642 гија 135640 ▁sør 135640 ▁બી 135639 ሩት 135638 ▁steder 135638 чении 135635 ▁doa 135634 ▁projeto 135634 ▁təşkilat 135634 ▁bell 135633 ▁संख्या 135633 ▁vajad 135628 บ้าง 135622 ▁toerana 135621 вили 135620 ▁դեպ 135619 ▁ចាប់ 135618 edin 135614 ▁anao 135612 حث 135609 өҙ 135607 ദ്യ 135606 დაც 135605 ልጣን 135600 upun 135599 ▁gestion 135598 ર્ટ 135593 เกิน 135592 asturianu 135590 ▁gjerne 135583 ▁Tren 135575 Op 135573 itsch 135567 ▁شپ 135560 ▁gagwe 135557 ክል 135556 ▁spørsmål 135544 tum 135541 ▁lugares 135536 ▁وفا 135536 ▁benim 135535 ävän 135533 ▁pokud 135532 ▁ستون 135525 ▁ဒ 135521 ņēm 135508 ▁dönt 135508 ▁Много 135508 ▁Sept 135504 顺 135504 bió 135496 ▁मेरी 135496 udha 135485 ▁honom 135483 ▁ਪ੍ਰਧਾਨ 135483 ▁հակ 135481 ▁rhe 135479 ▁thoirt 135479 ▁позво 135477 ▁sabiedr 135476 ▁bedrijf 135468 ▁geliş 135466 íoch 135460 써 135457 ▁kereta 135438 كتب 135435 ettek 135429 amata 135426 ysg 135424 ුණේ 135422 ▁ople 135420 upat 135413 ▁nesk 135398 ▁kirja 135397 plats 135395 ▁respon 135395 άνει 135393 લિ 135393 ▁sähkö 135390 ▁મહ 135390 ▁ரூ 135389 konna 135388 收入 135386 ▁Jab 135384 ▁esempio 135383 ▁uya 135378 vast 135377 åll 135374 oure 135373 ▁lucht 135372 ▁aivan 135369 ▁ਮਿਲ 135369 ▁حفظ 135367 ▁nana 135363 ลํา 135361 ▁Selangor 135360 aby 135357 ميز 135355 ▁bolje 135347 ▁tutul 135338 ▁Smith 135336 ▁fixo 135335 ▁atlet 135333 ЗА 135328 ▁Mwenyekiti 135327 ставка 135325 ▁παρέ 135321 İM 135320 ▁כש 135317 ▁Pablo 135312 ▁vindt 135310 ▁Ци 135309 चि 135307 ▁þær 135300 ▁Bli 135296 aad 135295 ုိး 135289 izk 135287 აჩ 135287 ไอ 135286 nicu 135285 ▁parl 135280 ▁आव 135276 ▁ഗു 135276 ▁artic 135275 ▁učestv 135272 ▁content 135270 nesi 135268 тры 135268 кої 135265 ▁vrat 135265 ंका 135263 ▁ლა 135261 ▁Cookie 135260 ▁sidii 135260 ▁XV 135258 ▁Türkçe 135250 ▁chw 135250 뉴스 135245 ▁ಕ್ರ 135244 ▁Ак 135243 uin 135239 ▁competi 135238 ▁Compostela 135236 šev 135233 alarini 135232 ▁կառավար 135231 按照 135231 ▁Αυτό 135229 ▁ગયા 135225 ψεις 135224 ▁دونوں 135223 ujących 135222 vem 135218 west 135212 եստ 135204 ▁sess 135203 ทั 135202 uak 135199 ▁ಬಾ 135199 arach 135198 ▁Gym 135197 ▁някои 135194 NING 135192 ديد 135187 UST 135186 가는 135185 աբեր 135184 ▁комфор 135184 λους 135178 alda 135174 ▁Lad 135169 сіі 135166 ayah 135156 ▁zdro 135154 ▁vợ 135151 ▁июнь 135149 夢 135145 वाल 135141 ▁silah 135133 럽 135129 効 135116 ಟೆ 135115 ▁هنر 135113 ▁soli 135104 الله 135099 浪 135099 ▁Activ 135095 idon 135091 ▁للح 135091 ▁ül 135085 ▁мү 135084 ívne 135083 note 135082 ▁outubro 135082 ່ານ 135074 不得 135073 áð 135065 ▁gaire 135052 etlen 135049 ▁àti 135049 ▁느 135048 ▁Gav 135044 араа 135041 веден 135032 ▁ადამიან 135029 ▁måneder 135026 ziwe 135025 ▁moito 135018 ▁onlar 135016 ▁уто 135014 ▁λειτουργ 135008 okban 134996 ▁mbola 134990 ▁zalo 134988 kua 134983 цт 134980 ▁Thế 134965 ฐ 134958 สนใจ 134957 בוע 134956 ράς 134955 ▁schonn 134954 ▁установи 134947 기는 134938 ▁рече 134928 ▁februar 134926 ▁ເອ 134925 ▁först 134923 ▁wetu 134923 ▁Москве 134921 ▁incon 134915 ▁Williams 134906 ▁качества 134903 ವ್ 134902 ଡ଼ା 134899 冲 134890 塔 134883 ደረገ 134881 ▁náš 134876 ապահ 134872 ′ 134866 ▁işlem 134865 ▁ngoNkulunkulu 134865 ▁น้ํา 134863 敢 134863 ლით 134862 ▁eN 134862 ėm 134854 ▁роль 134851 əyi 134846 ▁ofta 134844 ▁odlo 134841 пр 134839 ▁Raja 134838 ▁وچ 134833 つの 134829 ▁Hëllef 134826 ADO 134823 ▁Ancak 134819 ▁başqa 134815 ୱ 134814 ▁batang 134810 ଥା 134804 èlas 134802 ▁محدود 134801 ▁ɗo 134786 เส้น 134784 ektedir 134783 افي 134780 ▁Point 134780 ▁Yah 134779 ▁Τε 134779 ▁UT 134774 液 134768 ▁creo 134762 ▁Vær 134743 akita 134741 ଧାନ 134741 avam 134738 пуск 134734 ▁ibang 134728 ▁մտ 134726 amel 134725 ▁kroppen 134721 ▁Yak 134719 ▁godinu 134718 ɗen 134716 ▁cán 134715 ▁vyp 134715 ▁viongozi 134714 چر 134712 ▁csapat 134711 ▁Mani 134710 ▁sjuk 134705 ▁ukuran 134705 ▁веда 134705 ▁Liverpool 134700 mata 134699 ຮູ້ 134699 ORE 134694 ▁пост 134691 ▁104 134681 ▁bụla 134681 ▁kèk 134679 ▁canal 134677 Iz 134676 icznych 134673 වැනි 134671 ▁voľ 134669 ▁справ 134667 ▁czym 134665 online 134659 ▁slad 134657 ▁ง 134656 働 134655 ▁possibilità 134652 ▁Δή 134652 ▁kohe 134650 ▁имате 134647 zos 134643 ▁ರೈ 134643 พูด 134637 нити 134634 โรค 134634 ▁Host 134634 ▁барлық 134628 ▁apakah 134627 ▁mokili 134625 ▁golle 134624 ▁nauč 134624 携 134617 agd 134612 heds 134604 ▁rī 134603 dot 134601 ▁смя 134599 ▁агре 134596 ▁вести 134594 ▁sast 134593 ект 134591 紅 134585 ▁Ɗ 134583 ▁ଭି 134582 ▁abab 134581 ▁Qarabağ 134576 ▁телефо 134573 ffaa 134570 achda 134567 ▁nuevu 134566 timiz 134562 erten 134561 避 134561 อํา 134555 ▁miejsc 134555 ▁Eric 134538 ▁Beb 134537 hna 134536 ▁egentlig 134534 ▁soki 134532 OLO 134529 ▁HER 134524 ર્વ 134519 ▁90- 134515 :33 134514 ▁ashtu 134504 orp 134502 ISS 134499 ▁النا 134498 销 134497 风险 134494 ▁Κά 134489 ظهر 134481 ANC 134479 ▁põh 134479 ▁disco 134476 ▁خطاب 134476 loop 134475 avimo 134470 ▁znaj 134469 ▁тогто 134465 ▁erő 134463 Ing 134445 ട്ടെ 134443 ▁laatste 134443 ▁وهذا 134442 ியின் 134440 販売 134439 ▁mica 134437 ọng 134436 ▁bons 134434 മാണ 134430 ▁클 134426 ▁nový 134420 ではなく 134420 ▁сети 134417 ▁사이 134416 ▁Marka 134413 udem 134410 ▁загу 134408 ជី 134403 ▁Rav 134402 籍 134397 ้าว 134387 אָט 134385 jua 134378 ▁gede 134369 ▁كنت 134368 ▁ፓርቲ 134367 ▁ёстой 134366 ▁жоғары 134365 ▁كرد 134359 rage 134355 යකට 134353 ▁রাখ 134346 ▁arah 134338 ૪ 134332 ▁влада 134330 ▁bebas 134317 qet 134304 ▁TS 134303 상을 134303 имо 134302 ▁Kamu 134299 ▁Ауыл 134299 ρούν 134293 ujące 134289 osha 134288 ▁അല്ല 134288 ▁кеүек 134281 ▁vice 134279 ▁teñen 134277 ▁monte 134275 ▁domov 134270 ▁منصوب 134270 恋 134263 faq 134262 реш 134257 ▁konsa 134249 ▁ब्रा 134246 ▁posit 134244 šit 134242 тил 134242 ▁sumaganad 134236 ▁ضروری 134223 спи 134218 ▁Viv 134216 ▁okum 134213 ▁Wars 134208 ▁Тако 134208 ედი 134207 ▁1983 134206 vok 134204 ▁hjemmeside 134198 ▁Also 134195 ▁19- 134194 ▁свету 134191 ▁இன்று 134186 éch 134182 រួម 134180 ช่วง 134179 พี่ 134179 镇 134178 íma 134177 ▁කරන්නේ 134177 ▁Europea 134174 ▁programas 134173 ▁भि 134172 ▁យ៉ាង 134169 言葉 134169 ക്കം 134165 ambut 134163 ▁humanos 134146 ▁Muungano 134145 izay 134135 ▁groß 134135 ▁شدند 134131 ▁petits 134129 പോ 134128 ▁päivä 134121 ҡлы 134119 ▁1978 134118 ▁konferen 134112 rante 134108 ▁góp 134102 ਡੇ 134097 ▁ώστε 134091 riva 134085 ▁Bakıda 134085 ็อ 134083 ▁lengkap 134080 ▁zokugcina 134079 ក្ស 134077 arth 134076 ▁تعالى 134076 ▁وڌ 134075 غم 134067 ▁ਲੋਕ 134065 ▁Your 134063 ▁এখন 134063 itari 134056 不過 134056 Ą 134049 pool 134042 ▁мога 134038 ▁jeu 134031 Ass 134030 Gr 134030 韓 134030 ▁udvik 134029 දේ 134023 ▁многих 134021 ligheten 134019 妹 134015 ▁관계 134004 ▁POR 134003 ▁þessum 133998 ▁سیک 133998 ▁බලන්න 133995 ▁mandat 133993 siin 133990 ▁yami 133989 ▁Moo 133987 ▁champ 133985 ulit 133982 ▁izim 133979 ▁भन्दै 133977 лень 133971 ▁અહીં 133969 ጠበ 133967 uskan 133958 laridan 133957 éngt 133956 ってお 133947 ▁60% 133940 ▁નામ 133940 ▁Tuk 133935 ▁ouzh 133929 ▁සමා 133925 ▁өмір 133923 ▁spezi 133922 ▁toet 133922 ▁بڑی 133922 мые 133920 пові 133919 ▁музей 133919 ▁datingside 133909 ▁فتح 133906 ▁DH 133903 ▁هجي 133903 就会 133903 :32 133901 зара 133898 ▁tipos 133898 nok 133896 ▁update 133896 ▁сезон 133895 ▁потреб 133894 ▁Novi 133892 ▁questions 133890 их 133887 ▁elles 133887 ▁Kannada 133880 ▁dė 133879 ▁מצי 133879 ▁ternyata 133876 ▁เช่น 133875 тування 133862 ahaha 133847 alio 133837 luit 133836 ▁другите 133834 ▁Jorn 133830 ▁ઊ 133830 ▁fizi 133829 ▁mk 133828 ▁мова 133827 lius 133822 ▁prodotto 133819 บาล 133818 ሁን 133817 ▁सरकारी 133816 unal 133806 ▁-1 133803 мости 133802 ▁июль 133798 ▁იქნება 133797 ▁niez 133794 ▁بسبب 133794 ▁נוס 133793 ្សា 133778 ▁sige 133777 ▁কে 133777 ▁our 133775 ▁Laz 133774 וסי 133773 ▁થી 133773 ▁اصول 133772 ಿಸಿದರು 133768 ▁baadhi 133768 ▁muitos 133761 ▁БЕ 133746 ▁kertaa 133745 ▁spam 133744 escu 133739 ▁સમા 133739 nei 133737 ▁JE 133730 ത്തിനു 133714 ▁título 133710 ▁дока 133710 ▁свят 133709 ▁יוצ 133709 เธ 133704 ▁Học 133703 налы 133699 zyma 133696 оти 133694 ▁podría 133693 ▁(15) 133692 ▁fearr 133689 ▁mesi 133688 stadt 133682 ▁Castell 133679 ▁enne 133679 ảm 133672 ▁ձեր 133671 ▁досто 133669 ▁ഉള്ള 133669 력을 133664 ▁vəzif 133661 ▁repr 133660 mina 133659 ▁Три 133658 ዓለም 133656 ؤن 133653 ▁pani 133651 ▁मुख्यमंत्री 133651 odwa 133649 准备 133646 ▁میک 133645 ▁Kali 133641 കി 133640 ▁матери 133638 ▁મારી 133638 agol 133637 ▁עבודה 133634 verte 133632 ursos 133626 ▁техні 133624 ▁ติด 133624 ▁Fur 133619 سلام 133616 Yan 133615 ulli 133615 ▁guer 133614 ▁ದು 133613 ืน 133609 ▁kommet 133600 əll 133595 šwa 133593 ▁fib 133593 ▁оці 133590 ▁xeito 133588 ▁와 133588 upo 133581 ▁फोटो 133580 ▁Daftar 133578 ▁他 133575 ▁Mans 133571 saf 133569 ▁maneh 133568 ▁브 133563 র্ণ 133562 ធី 133557 ▁Yr 133549 ੰਤ 133548 ▁genoeg 133545 ▁позволяет 133544 ләш 133537 קער 133529 ▁alip 133525 სახ 133520 ▁piet 133520 ▁çıxış 133520 ยาก 133515 ▁nhiệt 133514 ▁teil 133514 ▁hjemme 133512 enja 133510 勇 133510 ▁všetkých 133508 position 133507 ▁ಅಥವಾ 133504 ▁İlk 133502 ▁vendar 133500 ңе 133498 Via 133495 ▁uzman 133492 ▁الدول 133492 ▁muhim 133487 fatter 133486 ▁სწ 133478 tikel 133476 ▁факто 133472 шил 133471 ▁להם 133468 ▁Ald 133467 ▁رشد 133466 による 133463 סן 133459 adanie 133458 нската 133443 etem 133441 ็ค 133437 ▁202 133437 ▁Міні 133433 бру 133429 ▁tëscht 133426 ▁інфарма 133424 ▁besten 133423 ▁olin 133419 ▁γε 133417 ▁ngah 133415 ▁секој 133409 ▁ovdje 133405 出的 133402 艺术 133400 ▁Familie 133399 ▁zákon 133399 гон 133397 ▁თქვენი 133389 pah 133387 比如 133380 ▁vọng 133374 ▁vede 133373 ▁Contoh 133371 ▁ਮਨ 133370 कीय 133368 ૌ 133367 ▁laika 133365 weit 133360 okwu 133352 ▁Beth 133352 ▁сабақ 133345 ▁১৪ 133343 ▁کافی 133337 ▁žup 133332 ▁पहि 133330 ▁පක්ෂ 133329 :「 133325 נון 133323 ▁joog 133322 ಾಪ 133317 ULA 133315 ▁vaja 133296 9- 133292 plica 133292 ▁selon 133291 ▁መካከል 133290 ▁увеличи 133286 ▁agenda 133285 હો 133274 ▁If 133273 ▁shug 133272 ▁fotoana 133269 ▁Vietnam 133267 Ì 133259 এস 133257 хам 133249 віча 133243 ▁Lamp 133241 いている 133238 -2015 133236 ▁خاصة 133231 жийн 133230 ▁проста 133229 ିଆ 133228 ▁děti 133227 ▁gom 133225 ▁ਸ੍ਰੀ 133220 ▁gah 133216 ▁වු 133211 ▁choix 133208 ▁ଅପ 133204 posti 133199 ▁လုပ္ 133194 ▁körül 133192 ▁prir 133190 ▁چونڊ 133187 ▁edin 133184 ▁Ober 133180 ▁express 133180 ▁Agent 133179 ▁galim 133179 ▁Resort 133166 ▁নিহত 133162 ▁meyor 133159 astur 133158 ▁नागरिक 133156 ▁መሆኑን 133156 ▁fået 133153 ▁baro 133149 ▁צום 133147 서는 133144 Jesu 133141 ▁Wirtschaft 133129 ทย 133124 ▁Укра 133123 ▁образование 133123 ▁Cik 133122 ▁Zd 133122 աճ 133118 アイ 133115 izzazione 133113 ▁Eis 133109 ▁toimit 133109 ▁keçiril 133107 ▁zgjedh 133106 nelle 133102 ▁richtig 133100 的事 133095 ▁விரு 133092 ▁▲ 133091 的工作 133090 ▁Yay 133089 druk 133084 انگ 133084 ▁געה 133084 ទឹក 133083 ▁полно 133079 ljene 133077 ▁ინტერ 133073 وير 133070 ▁RO 133040 ▁тих 133039 rato 133037 ▁áreas 133033 年来 133032 ▁θέση 133028 ▁використання 133027 ▁ସମସ୍ତ 133025 יים 133018 lando 133015 ▁mannen 133013 ▁Ext 133008 ▁Оде 133006 ▁높 133005 ▁корпора 132999 ▁ਮੋ 132995 ovun 132992 ▁Tamb 132990 ▁maf 132990 цэн 132987 ▁yapan 132987 ▁domác 132986 ▁ચો 132986 ▁മീ 132986 вните 132985 ▁correc 132985 授 132982 elmä 132977 ▁habit 132976 ▁început 132970 ître 132968 ▁хотел 132958 صه 132956 ▁aşağı 132955 ezione 132953 ▁tikrai 132950 ▁Cabugao 132946 ▁často 132942 ▁äl 132940 ▁כה 132936 ▁livello 132934 ▁rendel 132934 ▁sli 132930 ▁تعالی 132926 ▁ເຮັດ 132920 ijer 132918 ▁අස 132917 ▁አመ 132902 facebook 132901 ▁jamo 132899 endra 132897 asana 132894 ▁vezi 132894 ▁दृ 132894 ▁trek 132892 alina 132891 әх 132887 ▁положи 132886 nb 132885 ▁opge 132881 ▁fantas 132880 したり 132879 ▁nai 132876 ▁खर्च 132875 ▁ಸರ್ 132872 ▁före 132871 突然 132869 ▁chuir 132865 ▁geval 132865 ▁autob 132849 ▁sửa 132848 ▁માન 132846 ድን 132843 ହେ 132841 ▁segue 132841 ▁godi 132840 èrn 132835 ਐ 132831 ▁атмос 132830 ▁Նոր 132828 ▁мақса 132817 ▁party 132811 ▁уре 132809 ▁ettei 132807 ▁ფი 132806 蛋 132798 Ed 132796 чені 132796 ▁сит 132789 hairle 132784 γι 132781 ▁senaste 132779 üli 132770 text 132768 дим 132766 тат 132763 azana 132759 ციის 132758 하세요 132744 ▁Wasser 132740 ▁мяне 132736 ▁təhlük 132735 ▁은 132734 ▁thú 132731 ▁асо 132729 त्ती 132727 ▁վար 132725 elja 132724 ukų 132714 ▁entender 132714 迪 132713 ▁ambient 132705 ▁mwenye 132701 itive 132698 Го 132698 ▁கால 132697 ▁onders 132695 ▁сьогодні 132695 сат 132688 ▁lana 132688 に出 132684 ▁Кә 132680 ▁abdi 132679 føre 132674 ▁muerte 132673 жилт 132671 ▁Hyd 132665 ▁dàng 132663 ▁மன 132663 続き 132663 ▁želi 132655 劇 132654 СТИ 132649 ເພື່ອ 132649 ELE 132648 akov 132645 ▁BM 132645 tius 132639 ▁Hip 132639 ▁förs 132636 ▁персона 132634 负 132630 ▁минути 132622 kozás 132621 πού 132613 графи 132613 Ни 132612 ▁érdek 132612 ייס 132611 ▁diskut 132608 qü 132606 asema 132605 ▁handler 132597 hora 132591 ▁zavatra 132591 걸 132590 itoy 132588 ीत 132586 ُّ 132585 ▁hosp 132583 atsioon 132575 ▁веру 132574 ▁probabil 132573 ▁värld 132569 ▁Dus 132568 ტან 132566 ▁Qar 132566 vih 132562 ▁ansatte 132560 mud 132558 冠 132551 interno 132549 ▁այսօր 132549 ▁Visi 132542 ▁táv 132542 atorul 132539 ▁ഇനി 132538 ▁Dapat 132537 твори 132534 ▁kapan 132530 جار 132529 ประจํา 132523 최 132523 ▁zorg 132522 өрийн 132519 ▁maipapan 132515 ▁resol 132515 ▁učink 132513 ▁magaz 132511 ▁Maak 132508 cien 132506 ▁kasus 132504 ▁любов 132499 ▁coinc 132495 ▁أك 132495 ▁lokacin 132494 ດ້ານ 132493 ▁eram 132492 ▁لما 132490 ვლის 132482 ▁vje 132481 ▁एन 132466 ▁სადაც 132463 ñs 132462 ▁դեպքում 132458 ▁vš 132455 ▁літара 132455 ственного 132445 ▁qiladi 132442 ▁الات 132441 :34 132440 ▁সর্ব 132434 тке 132432 ▁санал 132430 ▁prøve 132424 ▁تومان 132418 ▁rík 132417 ▁studenti 132417 ▁максима 132417 ▁नीति 132414 ▁koppie 132413 ▁medicin 132412 ▁παν 132410 বাস 132409 зима 132407 ▁Munisipyo 132401 ▁VER 132400 ▁epä 132396 νον 132390 ▁potr 132389 aydi 132388 บาท 132387 力的 132378 bian 132377 ▁15% 132374 ▁Eusk 132374 ▁उत्पादन 132372 ▁کردیا 132364 ▁Æ 132362 ▁clase 132361 ဗ်ာ 132360 ▁saav 132357 ▁zokubheka 132356 ンを 132352 ▁milioane 132344 ▁Restaur 132331 ▁கார 132330 Ob 132327 ស្រី 132327 ▁Hej 132327 ▁mecan 132326 ▁tarixi 132326 ▁مدينة 132326 ▁Новини 132317 ▁impr 132312 ▁1975 132311 ▁konusunda 132311 älj 132309 ekto 132305 ▁уха 132302 ▁olabilir 132298 ▁ಪಾ 132296 ▁정말 132290 ▁nangruna 132286 νονται 132283 ▁дов 132282 arina 132279 ▁பொரு 132277 검 132277 ▁ਬੀ 132276 рау 132275 inoo 132272 ▁ваши 132271 ▁ਕਿਸੇ 132263 ▁ഞാൻ 132258 ▁Sekolah 132257 СЕ 132252 ▁गर्दा 132251 ▁Türkiyə 132249 אַמ 132248 ▁ତି 132247 ▁čist 132246 აშვილი 132244 ▁dahulu 132244 ▁евра 132244 ▁پښتو 132244 arium 132243 تال 132243 ▁apoio 132242 ŵ 132241 通知 132236 ▁პოლიტიკ 132235 ▁Бого 132232 ▁Brez 132228 ▁дэл 132227 ▁1982 132225 भाग 132219 ▁يس 132219 ▁मंत्री 132217 क्टर 132216 buru 132215 ▁رود 132215 ▁روغ 132207 တဲ႔ 132206 ეთა 132198 ध्या 132197 ▁utvik 132197 ▁diskus 132193 ությունից 132192 ▁priem 132192 》, 132192 laa 132190 ▁მეტ 132188 ▁sidd 132186 ▁техно 132182 ▁Marina 132181 زيد 132180 ვან 132177 ▁स्थ 132170 ▁GT 132167 ộng 132165 -23 132163 ாரம் 132162 czyn 132161 ▁fháil 132159 智 132159 ▁होगी 132158 KT 132156 ▁Chrome 132154 ▁големи 132153 ▁wäert 132148 ▁प्रकाश 132146 tuan 132144 ▁העל 132138 غة 132136 ▁fól 132133 ▁Mec 132115 个月 132111 ▁medlemmer 132110 κυ 132108 ▁fav 132106 ▁phản 132106 ▁ਦਿਨ 132104 тове 132101 ▁autora 132096 ▁ისტორი 132089 ▁πλή 132085 ▁хэлэлц 132085 ▁ගැනීමට 132085 ▁dimens 132080 ▁sentiment 132078 вій 132077 кна 132076 ๕ 132072 ୁଲ 132070 ▁Suba 132070 què 132067 ▁Сі 132065 lind 132060 ▁21- 132054 脱 132049 ▁европ 132048 arako 132047 ▁große 132047 ▁bíl 132044 ▁הגדול 132043 medi 132042 ▁φο 132042 ▁ച്ച 132037 ▁جيڪڏهن 132033 ▁ජනතාව 132031 ▁dry 132028 ▁çfarë 132028 ▁אותם 132027 แทน 132026 ቦች 132026 ँदै 132025 ▁פרו 132025 ▁periodu 132020 ▁rép 132019 olina 132011 ▁같 132010 ▁قلب 132008 ▁dager 132007 ▁260 132005 irta 132002 መስ 132002 ರೋ 131999 ▁bestimm 131999 ▁tuv 131995 ▁correspon 131992 zgar 131987 ▁facial 131987 ▁veng 131985 ▁மோ 131982 ચ્છ 131980 ▁SAN 131977 ▁hale 131974 ▁hicho 131974 зване 131969 elike 131967 ▁Dua 131967 ▁людини 131967 ▁नौ 131964 ▁ගැනීම 131964 ▁incer 131961 ▁aujourd 131959 ▁ert 131959 ▁ərzində 131956 ▁ກະ 131954 ულად 131953 ▁youtube 131953 védelmi 131951 ▁تاب 131951 hän 131947 ▁genre 131941 ▁yimɓe 131933 ▁томе 131931 ליט 131925 ▁merah 131918 ▁стал 131913 ▁المل 131910 ▁քաղաք 131908 hok 131907 ▁ארו 131905 hiya 131902 ▁qeyb 131902 መጣ 131894 ▁sosyal 131884 ল্প 131883 ியல் 131874 ▁Nok 131869 됐다 131854 ▁sababaraha 131851 ▁دوستان 131850 ▁ਮੀ 131850 ▁Fad 131849 imizi 131841 ▁večer 131837 況 131834 ▁الكثير 131833 ណា 131830 šky 131822 wały 131819 ፈው 131818 ▁fèt 131815 ▁dando 131811 јих 131810 ▁пациен 131810 ▁rámci 131809 ▁viik 131808 kathi 131804 терді 131802 ▁idan 131801 ▁изгра 131801 ciel 131800 ▁വാര് 131799 allah 131798 ▁potensi 131797 ▁белгі 131789 νοί 131778 ල්ප 131778 uló 131776 ▁ardh 131775 ▁വിള 131775 alag 131774 :02 131773 اعد 131770 ▁Oscar 131761 ▁bond 131760 ▁تعد 131760 ▁काठमाडौँ 131755 nieg 131750 ▁Тя 131747 ▁Mond 131745 ▁হচ্ছে 131745 גות 131743 ▁проф 131743 ▁Frances 131741 ▁wsz 131740 ्नु 131738 ▁principais 131735 上が 131732 ▁என்பது 131731 ویت 131730 ▁৯ 131728 ▁limp 131727 ▁геогра 131724 ೊಂದಿಗೆ 131723 චාර 131723 ിലാ 131719 ▁Alles 131719 oul 131714 real 131711 거나 131711 ▁kapa 131710 ▁farany 131707 ▁كار 131707 ▁nozī 131705 циялар 131701 isuu 131698 дем 131696 ▁কা 131686 üler 131678 あった 131671 ▁nyky 131663 ▁Hãy 131662 pré 131640 ▁käyttää 131638 تیا 131632 ▁ramach 131626 ▁Kala 131625 ▁tarif 131624 مون 131623 ▁2006, 131621 ▁Avrupa 131616 ▁matem 131616 лив 131615 राव 131607 ▁contemp 131607 ▁gihapon 131605 šanā 131601 ▁måned 131597 ▁পে 131592 ▁अभ 131585 ▁dát 131584 ▁oj 131584 ▁الآن 131582 응 131581 ▁Фор 131580 ▁yén 131579 ▁racc 131576 ▁अर 131574 льне 131572 ▁ლო 131572 ▁TU 131571 ▁የበ 131568 ▁ukuthini 131567 chaften 131563 ▁정부 131560 дым 131558 рени 131557 сної 131557 ▁sikre 131554 ▁totale 131553 ▁عالی 131553 ▁귀 131553 พ่อ 131549 arem 131548 ▁ёсць 131547 ▁אדער 131540 はい 131539 ▁Tại 131535 ▁tiyan 131534 ▁କରିବାକୁ 131533 โรงแรม 131529 ▁haur 131527 ▁calor 131526 ▁regn 131526 ▁finalmente 131525 elių 131521 ▁Cent 131519 ▁महिना 131519 ربع 131515 ਾਪ 131514 ▁โรงแรม 131507 innya 131506 новя 131505 avis 131503 ведение 131503 ▁έτσι 131502 ▁собра 131502 كرة 131495 мки 131489 ▁hitaande 131488 ▁Бел 131483 zean 131482 ለች 131480 ▁аҙ 131479 ▁6) 131467 ▁нап 131467 الف 131465 ▁håll 131460 ▁ਬਣ 131459 ▁browser 131457 тыра 131456 ▁Toate 131453 дарында 131452 挑 131452 ▁Между 131444 issent 131442 աչ 131442 ▁वापर 131441 ▁τρόπο 131431 ▁odnosno 131427 ▁tila 131426 ▁મારા 131418 課 131402 пай 131401 පති 131401 ▁lasang 131400 ▁наси 131399 ▁ematen 131391 ▁كم 131391 ▁qadın 131383 izioni 131376 ▁බවත් 131359 らず 131359 ▁wiy 131355 ▁праці 131353 ▁پزشکی 131348 krit 131347 сындағы 131347 ▁ಇದ್ದ 131347 ▁ຮຽນ 131344 お客様 131343 ▁biyu 131341 ▁ေတာ 131340 ▁бий 131338 bá 131331 ▁Roh 131323 แนว 131322 ▁बड़ा 131317 ▁שלכם 131316 ▁በጣም 131313 बीच 131310 ▁setiembre 131304 宣 131304 льнай 131301 κης 131299 ▁Sko 131298 ▁یون 131297 ▁azal 131295 ▁wygląda 131288 诺 131283 iyyar 131281 ▁Chelsea 131281 ▁hivi 131281 ▁level 131275 ▁сро 131274 ▁개인 131274 ibt 131273 ▁posebno 131270 igner 131268 ▁ඒවා 131268 ▁jouk 131263 ▁raf 131252 ▁საქ 131252 нім 131247 asiy 131246 ▁laa 131244 ▁perten 131240 ▁jeb 131237 щая 131236 ▁ಕೇಂದ್ರ 131235 ăs 131234 oil 131232 ▁당신 131232 niad 131230 ▁μεγαλύτε 131229 لاس 131222 ▁diversi 131218 小时 131218 ▁którego 131214 ▁यहां 131204 ▁تصاویر 131203 ▁बाँ 131203 قق 131200 kost 131199 ▁fortsatt 131199 ostat 131198 ▁Wahl 131197 etje 131195 ▁Sommer 131194 λια 131187 ▁홍 131184 ▁hende 131180 ეც 131179 ▁Even 131179 kārt 131168 ▁واست 131164 ▁árið 131162 ▁carrera 131160 ▁являются 131158 ▁କରାଯ 131156 一度 131148 gina 131143 ▁aport 131142 çilər 131136 زب 131131 ▁jire 131131 ▁құра 131129 гів 131125 ▁bè 131124 ▁thơ 131123 ▁ဟ 131123 ▁нер 131122 ቼ 131119 şe 131118 ▁tarpe 131118 łę 131113 ▁பற்றி 131112 ▁أفضل 131107 Sathane 131103 ▁götür 131103 ▁Pere 131102 ▁fecha 131102 ▁acaba 131101 ▁මූ 131099 ▁meet 131098 lecht 131093 ësch 131093 ▁mineral 131091 തിന് 131090 ដឹង 131088 ▁sommes 131081 okoa 131080 ▁mieć 131079 新闻 131076 ▁vitit 131075 ▁dhab 131073 idik 131071 anyika 131070 ▁Оп 131069 ▁itali 131068 ▁месте 131067 ▁Ref 131064 elah 131063 lichkeit 131054 ▁ആണ് 131052 ยิ่ง 131047 ล่าสุด 131041 损 131040 ▁концер 131038 ▁kraft 131037 ▁ମାନ 131029 ▁bụrụ 131018 ▁schop 131016 neet 131012 ້ອມ 131009 ებოდა 131007 ▁ฮ 131006 űr 131003 endum 130999 όμενο 130996 ▁suke 130996 ूट 130994 ▁തിര 130989 Те 130987 beeld 130985 onya 130981 娱乐 130978 ▁molti 130977 əc 130973 ▁Ner 130966 灵 130965 ▁filmu 130964 ियर 130963 šten 130962 ▁پری 130955 эта 130952 ikä 130946 tras 130946 ിച്ചത് 130946 нау 130943 ёл 130939 ¿ 130927 نامه 130924 ▁zako 130922 ▁ayuda 130921 ▁răng 130918 legen 130913 制造 130896 ويل 130893 ohjel 130885 ▁Wid 130883 ▁обезбе 130881 ραν 130874 ▁ہوگا 130872 ▁Law 130871 քով 130868 ▁સરકાર 130863 ତର 130862 ▁xog 130861 ▁сър 130860 ▁saug 130859 ална 130853 waj 130851 ▁யா 130851 ህን 130848 benar 130847 ▁сек 130847 ▁اینجا 130845 ▁nghiên 130841 ▁október 130837 itelja 130834 ▁आग 130834 ታል 130833 ▁സാധ 130833 ▁temper 130832 ellem 130831 щия 130828 emd 130826 ▁পুর 130826 ▁Buna 130823 ▁transforma 130821 лікті 130818 ▁possa 130818 —19 130813 ▁đứng 130811 ▁volver 130803 ▁aastat 130801 ඒ 130800 ▁kesä 130799 ▁иску 130799 ▁המר 130793 ākā 130792 ▁Nasional 130790 ագույն 130784 ▁അട 130784 ▁concert 130783 ▁ऊ 130783 အစ 130782 lede 130780 ▁engen 130773 okub 130772 ▁hainbat 130771 ▁ඇය 130770 ▁campaña 130764 ▁Bluetooth 130763 ▁komplett 130762 yczny 130761 ▁1,3 130759 ▁70- 130757 ▁pasangan 130757 ▁журналі 130757 ▁छु 130754 ▁Kaikki 130750 ▁miti 130750 isay 130745 ખો 130741 ▁dinge 130736 ▁siete 130735 ▁મન 130735 ▁چگونه 130731 ▁ඔබේ 130727 ▁اختيار 130721 omina 130719 ▁रोज 130712 ▁ਬਾਅਦ 130711 ávajú 130708 ▁පුද්ගල 130707 циялық 130698 තින් 130697 ▁beta 130689 ▁metri 130681 ugat 130680 חס 130676 ryw 130674 ▁ձեւ 130674 ڍي 130673 čného 130669 بہ 130661 obu 130643 ▁piger 130643 ▁ocupa 130640 クリ 130639 ▁χά 130638 unit 130637 ▁avanz 130630 wej 130622 ▁chẳng 130622 ▁þessa 130622 ▁Tư 130615 :37 130609 iał 130606 ግብ 130605 ▁پوسٹ 130604 ▁छि 130590 ▁novembro 130589 ▁همکاری 130589 AMP 130585 astro 130583 ▁Kid 130573 ▁fear 130571 遭 130571 ▁управления 130569 ▁Lebih 130558 idl 130550 ▁książ 130549 ▁συγκεκρι 130547 ▁လက္ 130547 āni 130543 ▁хаҡында 130543 льд 130542 ▁aprender 130540 ▁Аз 130537 ▁éischt 130524 ▁어떻게 130519 ▁Masa 130514 ▁hərbi 130509 ਿਆਰ 130506 үм 130505 ਟੇ 130505 ພວກ 130505 lel 130503 ▁XXX 130500 ▁ženy 130499 ▁станови 130497 ▁जय 130491 ▁emis 130489 alisa 130485 ▁silpo 130483 ▁byli 130482 ▁मिला 130470 ଜା 130455 ▁şu 130454 ▁Heydər 130452 ▁അവര് 130449 ▁Mwan 130445 مہ 130442 ▁erkl 130442 一切 130442 vaid 130441 ▁naszych 130440 ▁Computer 130436 ଯ୍ୟ 130427 載 130418 ਿੰਦ 130415 ▁Chris 130414 खी 130413 ▁ຫວຽດນາມ 130404 暖 130404 ▁לאי 130403 ▁વધ 130403 ází 130402 ▁هئ 130402 ▁Sida 130399 ▁dise 130399 ▁оди 130396 梅 130396 leo 130389 ▁Pendidikan 130385 ▁razem 130383 ▁ክፍ 130383 人生 130383 ▁sahib 130381 ▁bergen 130378 ▁Kauno 130363 ▁Phòng 130356 Book 130355 ▁nazo 130351 杀 130345 یتی 130344 ▁Qeyd 130343 ▁ruang 130335 ▁dicir 130334 ▁qabsoo 130328 ▁дауам 130322 ங் 130321 ▁студент 130320 тран 130317 ▁കഴി 130317 ▁Eğitim 130315 而是 130315 ▁पटक 130309 ▁ተቀ 130308 ministr 130301 ▁integ 130296 abab 130295 ঝ 130290 ត្ថ 130290 ▁Industri 130288 ளவு 130282 ▁103 130282 ▁ию 130277 ėms 130275 ▁대통령 130274 τίας 130272 ▁iDNES 130266 ▁تمامی 130259 ▁prá 130257 ▁Ever 130253 ▁opet 130252 ▁bund 130250 ▁росій 130250 ▁infrastruktur 130249 рис 130248 ▁MER 130247 ▁izvr 130246 ▁znám 130244 池 130242 ▁Oku 130241 ▁лечение 130240 klen 130234 ▁slå 130234 remos 130230 ▁ejemplo 130227 ▁аналі 130227 vill 130222 ▁:)) 130216 hamba 130212 ▁presentación 130212 ▁Rama 130208 ▁glu 130208 лада 130206 рық 130204 ▁Հայաստան 130204 ώντας 130201 ▁müxtəlif 130190 ▁измени 130190 ვას 130186 化的 130177 ▁tonë 130172 джу 130161 ▁sukker 130156 ▁אַלע 130148 ▁Kabupaten 130145 ▁rov 130144 Ų 130140 ▁दर् 130137 ▁בסי 130136 ▁החל 130132 ▁odpad 130130 貨 130127 ไหม 130126 ▁век 130126 vény 130122 ▁parha 130122 ▁1,6 130119 ക്കോ 130117 ▁ομάδα 130116 tri 130108 andik 130102 ▁ನೇ 130091 خو 130086 ើង 130083 alai 130081 ▁ڊي 130081 ▁ಮಾಡಿದ 130081 ▁bann 130079 ▁misma 130074 æring 130071 ranu 130070 亿 130068 érica 130065 ▁turiz 130065 เขียน 130062 ▁kubo 130057 ▁egyszerű 130056 ▁tərəf 130056 cons 130050 χής 130048 equip 130040 ▁회사 130040 ▁ие 130038 ▁prác 130036 ▁head 130035 களில் 130032 ▁Occ 130024 염 130022 чына 130021 ▁west 130017 ▁போல 130015 ρίας 130014 ▁좋아 130011 ▁شاع 130009 ungt 130007 isteri 130005 ਪਾ 130002 简单 129995 ▁којима 129994 ประกอบ 129993 ▁внеш 129992 ▁משפ 129990 ▁1968 129988 ▁nächsten 129988 ▁Mëtt 129983 dah 129978 elayo 129978 klı 129971 ▁roedd 129961 ▁Tet 129960 ▁Local 129959 Ke 129958 ▁yoxdur 129957 ▁wei 129952 ▁соответ 129950 Ko 129944 ▁pře 129939 פאר 129938 ▁حيات 129935 เสริม 129934 ικα 129927 ▁pista 129924 ▁пай 129923 ▁skjer 129921 ▁ሕዝብ 129914 ▁izwi 129912 ▁επίσης 129908 ▁jakie 129904 ▁menny 129904 ▁Forma 129897 ▁cores 129897 ▁বার 129896 ▁ει 129891 гове 129888 ▁леп 129886 ▁Code 129883 万元 129882 さま 129871 ▁matin 129870 ▁goeie 129866 ▁สุ 129865 ▁okub 129864 ▁asegur 129860 ▁سع 129857 ▁कर्मचारी 129857 ▁자신 129857 ▁iyadoo 129855 スク 129855 宅 129855 ▁өөрийн 129850 ▁መን 129850 ▁Ваши 129847 ຄ້າ 129845 ▁的 129842 ടന 129840 ▁etwa 129839 çada 129836 ▁맛 129834 ▁Փաշին 129833 ▁условиях 129831 ▁породи 129830 ریه 129829 práv 129825 ▁ଠ 129824 "> 129819 валася 129819 แบ 129815 ▁samh 129814 áveis 129812 ▁samun 129809 ▁Company 129808 ▁perk 129808 ▁ലൈ 129800 :36 129799 ▁verði 129798 सले 129796 ▁Ketika 129795 бил 129793 ▁승 129793 ▁māj 129792 ▁উদ 129791 aries 129785 ▁Apakah 129782 થા 129771 推动 129768 ▁vreemdelinge 129767 ▁spar 129766 ▁MC 129765 層 129762 ▁Tallinna 129756 ▁בתי 129755 ▁ancak 129754 怀 129750 utada 129749 ▁모습 129748 ▁Hotell 129745 ▁dernier 129735 けない 129734 ोप 129732 탄 129732 แฟ 129730 ▁gammel 129727 ▁reda 129727 ▁arqu 129726 ത്രി 129724 šnja 129723 කය 129722 ▁García 129720 끼 129718 тво 129715 ▁pagpap 129714 кама 129709 索 129709 jale 129707 ▁akibat 129707 ipi 129702 ▁budak 129699 gw 129696 ▁vezető 129687 ▁Edward 129684 ▁néhány 129675 यत 129672 やって 129672 よね 129670 ▁kritt 129669 ▁svojo 129667 вались 129663 ▁ಕೇಳ 129658 cesso 129654 ▁cavab 129654 ▁měl 129653 ően 129651 ▁bals 129651 ▁ਆਪਣੀ 129650 ▁visual 129648 ▁vars 129646 ▁plasti 129638 ▁കൃ 129633 ูน 129631 ခ်ိန္ 129629 ioa 129627 ▁alumnos 129624 特に 129617 ▁thug 129615 ▁երկու 129605 ▁fid 129602 ▁игры 129600 ▁Bom 129594 လက္ 129586 ▁tru 129585 ▁особено 129583 oooo 129582 ▁jullie 129582 ಳೆಯ 129579 ▁යාල 129574 ▁жал 129572 кое 129571 ▁خوبی 129570 ▁nieuw 129564 ▁Сите 129561 ▁մեզ 129560 ▁nalazi 129558 વાનું 129557 ▁1977 129555 ିକ୍ 129552 ▁প্রযুক্তি 129551 ▁db 129549 ▁contempor 129548 ជ្រ 129546 违 129535 刘 129532 στι 129530 ጠው 129527 ▁svijetu 129527 めた 129527 ytas 129526 ▁Fah 129524 дник 129521 ▁besky 129517 ▁kommentaar 129511 ▁watoto 129504 ▁Quận 129502 ▁ubyt 129496 álni 129494 ଦେଶ 129492 ญี่ปุ่น 129492 ▁። 129491 vare 129488 ijesti 129483 ਤਾਰ 129483 ▁spesso 129480 নায় 129479 ▁чув 129479 под 129475 ▁През 129475 ▁Sip 129474 وح 129466 ανε 129464 ▁book 129462 ▁เธ 129462 મને 129458 alahan 129456 ▁ଦେଶ 129451 intis 129450 ▁uut 129447 ▁алмай 129446 ▁ସାମ 129443 ▁ڪڏهن 129442 ▁ngisho 129439 値 129431 ▁arbeids 129430 ▁termék 129407 ▁خلکو 129403 ▁locul 129400 ▁prø 129398 verket 129397 ▁இந்திய 129391 ।” 129390 ▁reper 129389 ▁береһе 129386 ▁ব্র 129385 ▁draud 129384 ▁شیر 129383 ▁menyediakan 129377 ▁setembro 129376 дера 129375 ▁texnologiy 129369 ▁Кө 129364 ▁כר 129363 ುತ್ತಿದೆ 129361 шкі 129356 meld 129355 ▁Udal 129351 ▁сили 129348 ▁ေတြ 129343 ▁XXI 129341 éro 129337 ▁اللا 129332 lege 129327 ▁Boleh 129324 ▁rå 129320 ılık 129318 ▁kuts 129313 font 129308 ಾನ್ 129298 ▁konnen 129296 ▁täi 129290 ▁демон 129285 ▁домаш 129284 資料 129281 ishni 129277 ገራ 129276 ▁presenza 129273 ▁riječ 129272 ▁abil 129269 କ୍ଷା 129266 隨 129265 ografía 129259 ▁сцена 129256 ▁wonde 129252 ளுக்கு 129250 нымі 129247 ຊາ 129244 9] 129241 あれば 129240 ប្រទេស 129237 ▁pessoa 129234 ▁Party 129226 ▁reiz 129226 ▁Бал 129219 fique 129215 ▁ശ്രീ 129214 ▁1.2 129207 alten 129204 ▁jestem 129202 ▁lac 129193 ▁Fiz 129191 ▁пъти 129186 ▁decembra 129184 ▁រឿង 129184 ») 129183 ÖR 129180 ▁niyang 129179 .« 129177 ▁fannt 129176 ▁સ્વ 129174 のご 129170 pà 129168 ▁Stock 129158 ▁qat 129158 ▁зашти 129157 вэл 129156 ▁വെള്ള 129154 ▁üh 129153 ▁1941 129151 ▁стана 129151 ▁אף 129147 ▁සම්බන්ධ 129144 inzi 129139 ▁सम्पन्न 129135 ▁clás 129133 ▁nền 129131 sian 129130 連絡 129127 ▁giấy 129125 ▁rhoi 129111 投資 129108 ▁yim 129100 বাদ 129098 ▁Selamat 129096 ▁nhạc 129094 ठे 129091 ▁પાસે 129091 乳 129084 02 129083 ▁суспі 129082 ▁libros 129080 ുകയും 129078 ▁Oz 129078 ▁ຫນ້າ 129073 يش 129069 ▁12:00 129069 ▁-2 129068 ▁etdiyi 129066 ▁hakim 129063 ▁welke 129062 сник 129060 สมัคร 129058 들과 129057 ארט 129055 čem 129051 ▁køb 129050 väl 129049 ▁menemukan 129049 ▁önc 129046 ▁الأربعاء 129045 რთ 129040 ▁Fire 129038 скоп 129032 ▁poner 129030 ▁meste 129027 ຕົວ 129024 ▁Београд 129017 ▁Kül 129016 ▁jai 129015 ▁μέσω 129013 ▁Burg 129012 ▁tempor 129012 ▁ран 129004 heb 129002 Ин 129002 ▁LT 129000 ▁Axmed 128999 ලය 128998 ៉ា 128997 ▁mura 128997 ▁semula 128996 ínu 128995 ▁լր 128995 ▁mērķ 128993 幼 128992 ▁отворе 128987 ይወት 128984 პარ 128980 camp 128971 ிலும் 128970 ပါး 128970 ▁jopa 128969 ▁жылғы 128969 ▁سواء 128969 ▁helyzet 128968 ▁کھیل 128965 სახურ 128964 ▁үҙе 128962 njo 128958 ▁yarış 128955 ▁ગો 128951 ▁Reis 128949 ▁normale 128949 ▁ώρα 128949 عادة 128948 ▁concentr 128947 ORI 128946 ▁ගණ 128946 स्त्र 128944 対応 128944 ▁հոգ 128937 ▁ਮਾ 128936 ladı 128932 ▁изо 128931 ▁ах 128928 ▁کله 128927 zania 128912 ▁İslam 128904 ▁เห 128904 ▁امریکہ 128903 ୍ଵ 128901 封 128898 ilai 128892 ▁праця 128891 岸 128890 ▁зохи 128889 ▁Pusat 128881 ▁disini 128879 ▁terutama 128876 ▁ING 128873 ▁دسته 128873 ▁baita 128871 ложения 128865 adala 128860 ወሰ 128860 ▁ketdi 128859 ரில் 128858 ▁wyr 128856 jaar 128855 ▁pyst 128855 ▁سمج 128855 ▁ahayd 128853 لمات 128851 க்கொ 128839 ▁السي 128835 ▁sogar 128831 ▁insieme 128829 ▁tugas 128827 ▁भु 128820 ▁دهید 128819 都不 128819 ▁دھ 128817 LAN 128815 ▁भन्दा 128811 ერის 128807 ▁tịch 128807 uvad 128806 จะมี 128804 ▁550 128802 ▁الدولة 128802 ▁مکمل 128799 ▁wac 128797 ગર 128796 מען 128795 купа 128792 ふ 128792 ▁දිය 128790 パー 128790 ▁opisk 128786 ▁বাস 128786 ▁Onun 128781 ▁ikon 128781 ▁وزیراعظم 128778 idla 128776 ▁atom 128776 ▁તેની 128774 étt 128772 厅 128769 उँदै 128756 ▁κε 128752 ▁труда 128752 ▁upo 128746 ▁շրջ 128746 ▁Ron 128745 ▁सहा 128742 ▁perg 128735 eves 128733 ▁Berikut 128729 ▁بلوچستان 128727 ☆ 128727 ମ୍ଭ 128724 ▁POS 128721 üp 128720 idence 128719 ▁기능 128717 ▁വിശ് 128716 ▁hamb 128714 ▁প্রথম 128710 ▁πρά 128707 えた 128705 тайте 128700 ▁жаз 128697 ▁dobra 128696 ▁ٻه 128695 ▁gobolka 128692 ាស៊ី 128688 ▁грани 128686 ▁이름 128681 nalist 128678 ▁tarih 128678 ▁barcha 128675 ▁offici 128672 ▁wod 128672 ▁электро 128672 ída 128669 жны 128669 ▁світ 128666 ▁straips 128660 ▁tällä 128660 ▁ടി 128657 ▁ibilbide 128647 entzat 128643 ▁аймгийн 128643 ਰਮ 128640 문화 128636 ▁obres 128628 ▁ಬೆಂಗಳೂರು 128624 ▁обяза 128621 zana 128616 ▁kolme 128613 ▁прийня 128609 ▁əlavə 128607 ēti 128606 してく 128601 മെ 128600 ▁zdję 128597 ▁днес 128592 ▁твър 128581 ẹn 128577 ▁XIII 128575 につ 128574 inie 128573 ▁großen 128566 ▁înt 128558 ▁میزان 128558 ಿಕೊಂಡ 128557 នោះ 128554 ▁Disney 128553 ▁deben 128551 ▁conseg 128548 ხს 128543 дарға 128540 ▁млади 128540 まま 128540 ▁მკ 128538 ▁מומ 128535 тыя 128534 ̃ 128532 ▁সম্পাদক 128532 ▁ahogy 128531 ▁bertan 128530 ▁velike 128530 ▁Rück 128528 ▁hvorfor 128528 ▁spole 128525 ▁delen 128524 тый 128522 ▁Gobierno 128522 ▁своє 128522 ളെ 128519 ▁Rosa 128511 ▁vost 128511 ▁култур 128506 ว์ 128505 :42 128504 ▁여성 128502 ▁dofir 128498 ▁Fant 128491 ▁ιστο 128491 šenje 128486 ▁מקום 128482 μούς 128478 ▁určen 128477 ▁xəst 128469 ▁oon 128467 kamer 128466 ▁bổ 128465 nicima 128462 сной 128460 ▁міжнарод 128457 ▁ಗಳ 128455 niques 128448 кр 128448 ▁indlæg 128446 žev 128441 ▁adlı 128436 ▁आला 128432 veno 128430 ▁пару 128425 ▁Sebelum 128424 莫 128418 ▁fha 128413 abwe 128409 յա 128407 ваецца 128404 ▁сп 128401 ▁Komentar 128400 ▁گیری 128399 ▁azali 128398 ▁heti 128395 ▁настав 128393 ୃତ 128389 ▁subven 128386 ▁أمام 128386 ▁тикше 128385 stil 128379 ρασ 128378 yss 128375 ▁Egip 128375 ▁άλλο 128367 ▁jarra 128365 ▁ਪਹਿ 128365 ຈໍາ 128363 持ち 128357 ▁tránh 128353 ฆ 128352 ▁wus 128345 ený 128336 ▁ün 128336 ▁մինչեւ 128335 ▁филм 128330 ľad 128326 ▁teror 128326 ▁चित्र 128323 ▁інформа 128321 ▁مقصد 128320 ▁taču 128319 ▁ਹੁਣ 128318 ▁πάντα 128315 ▁શિ 128311 zut 128307 ेंबर 128307 อี 128301 ▁የታ 128298 ▁eti 128296 봉 128294 ▁Ár 128293 何か 128293 ▁פּראָ 128290 ▁veta 128281 ▁ଭାବେ 128281 ▁Ukra 128280 ▁dijel 128278 ▁пъл 128278 zda 128276 ▁תר 128274 ▁offr 128271 koh 128268 ▁ምክንያት 128265 ťa 128263 ▁τελ 128259 баты 128258 ▁ಅನು 128251 ▁बड़ी 128250 arska 128248 ▁Administr 128247 ▁ಅಲ್ಲ 128244 ▁অন 128240 ▁СО 128237 ▁SY 128235 亞 128233 кур 128226 ▁lingkungan 128222 ▁تبد 128221 ▁inklud 128218 ▁Polis 128217 нина 128216 ▁eveniment 128216 ▁کمپ 128214 šī 128212 ▁Avi 128212 ▁wh 128210 ሱን 128202 rta 128200 ▁NASA 128199 ▁màn 128198 ▁людзей 128195 ▁viszont 128193 tgan 128188 isiúnta 128184 繁 128182 ▁usando 128181 ▁বাড় 128178 ▁स्था 128174 uteur 128170 aket 128166 arai 128163 ▁கருத்து 128162 aní 128161 enh 128160 ▁характери 128159 ▁طی 128151 ▁गि 128150 ▁espect 128148 prava 128140 ▁Русија 128135 ▁esas 128134 ▁പ്രധാന 128133 大きな 128133 ▁oble 128131 ▁информацион 128128 ▁વર્ 128128 нія 128127 ▁упа 128121 iraju 128119 koo 128111 ▁শেষ 128111 ▁أما 128110 ▁الأمر 128110 ▁þátt 128108 ▁thôi 128105 letes 128103 ністю 128096 ▁indica 128094 ▁pozit 128091 ▁Hud 128082 ▁ಮನೆ 128082 ▁século 128067 ▁deles 128066 ▁नं 128065 ▁pacient 128058 ▁کرے 128057 ▁Wr 128056 ▁прото 128056 ▁լավ 128056 ▁تض 128053 ેશન 128052 ynta 128051 ▁दश 128051 ▁obst 128049 ▁полностью 128049 ▁virtual 128047 ቱም 128045 ▁Hollywood 128044 ▁tienes 128044 专家 128043 ▁beag 128033 ▁работает 128029 ▁اهل 128028 ▁јавно 128019 amd 128018 奶 128013 ▁fica 128011 aaaa 128009 strat 128009 ▁bitte 128009 ДУ 128006 ▁Пі 128006 ▁dificult 128005 美國 128001 yra 127996 ▁abs 127993 ▁سنة 127990 ▁ក្រោយ 127987 bing 127985 ▁predaj 127985 ▁ervaring 127984 ▁består 127982 ▁mjesta 127981 ▁Fed 127972 ▁ото 127970 ▁असेल 127970 ▁වර 127967 ▁따 127965 ▁רוצה 127964 ▁الخاصة 127961 द्र 127960 웃 127956 ▁završ 127949 énk 127945 ▁שאת 127945 γραφή 127944 ംഗ് 127937 ▁taşı 127936 vard 127934 ▁2003. 127934 ▁amihang 127931 bib 127930 hæng 127927 lini 127927 ▁службы 127927 iono 127925 ኢትዮጵያ 127925 ▁opini 127925 inoma 127920 ▁jogo 127915 parti 127914 ▁penn 127911 ▁Galega 127910 ይል 127908 െയ് 127905 ▁رابطه 127902 ▁रुपये 127898 雅 127894 ▁વાંચ 127891 որեն 127890 طب 127886 ▁regoor 127883 ▁שלה 127882 ▁cham 127871 uet 127859 ▁thuận 127857 シー 127853 վելու 127849 unc 127847 wele 127846 ▁Schw 127843 тной 127842 ▁força 127842 БО 127841 われる 127836 აძე 127834 ▁jednej 127831 ην 127829 ▁Thời 127828 stwa 127822 ▁mhe 127819 ▁1,4 127817 ियां 127808 ▁Helsinki 127808 ▁яуап 127808 ▁Xem 127806 ▁කරනවා 127804 ▁daran 127802 orter 127800 ▁azad 127799 ▁Теле 127797 ыр 127796 ▁ҚР 127796 unque 127793 ڪس 127788 ▁részt 127787 ▁φί 127782 ▁ડિ 127778 лното 127777 ▁esetén 127775 ▁yerine 127769 参考 127769 ظه 127768 מח 127764 ▁SN 127763 ▁зер 127758 ▁можли 127758 ▁elet 127757 ុល 127754 ▁cole 127754 然而 127753 ▁gift 127749 ▁Dzięki 127748 ▁Salvador 127741 ▁бола 127741 ţilor 127737 ▁funks 127737 ▁õig 127731 ▁salari 127730 ബന്ധ 127727 ▁ആര് 127721 ▁ខ្ញុំ 127720 ilean 127715 ▁Platz 127713 ▁aumento 127713 ▁SB 127709 ▁ngale 127705 ▁Ethiopia 127702 ▁informacji 127698 एन 127694 età 127692 ▁olvas 127692 времен 127684 toja 127682 ників 127681 ▁নিজ 127676 ▁starp 127672 ovú 127667 اتف 127667 មុខ 127667 ▁mada 127667 อายุ 127665 ို႕ 127663 يث 127662 이며 127659 jècte 127656 ▁interesante 127656 mən 127648 onakala 127646 ▁allan 127643 éierung 127642 ลูกค้า 127640 ▁Reh 127639 童 127639 queta 127636 ▁leit 127635 ▁अघि 127633 ▁მთ 127633 .00 127629 iddi 127628 dali 127625 ilg 127625 ▁Madax 127625 ▁lavil 127624 ▁ամբողջ 127623 ▁neh 127618 ▁prend 127613 되고 127613 akathi 127611 дну 127610 ▁Seyè 127609 apro 127608 ▁PEN 127602 ▁слід 127599 ▁roh 127597 ▁dzień 127593 egd 127592 ክር 127588 ▁stel 127588 もあります 127588 并且 127579 ▁انتقال 127569 gie 127567 ▁εμπ 127563 一种 127562 ▁Kav 127556 ▁Lego 127553 ıc 127547 ▁بلند 127547 ▁müs 127540 ▁19:00 127539 গঞ্জ 127538 ▁Lands 127536 ▁DN 127533 ▁ରହ 127532 ობი 127528 կեր 127526 罪 127525 व्ह 127524 ▁키 127524 ▁College 127512 ▁povinn 127510 ▁début 127503 ▁meeste 127503 cute 127493 ಂಟ್ 127492 เท้า 127492 ▁Valen 127487 ▁പങ്ക 127485 ▁želite 127483 idat 127482 ▁isol 127478 ▁орын 127477 并不 127477 Estat 127474 ▁uits 127469 多くの 127455 ▁Mom 127452 ▁ستر 127447 ▁Улаанбаатар 127444 ▁zár 127438 ▁заво 127436 ▁insanlar 127435 erit 127434 ▁iarraidh 127434 ▁cream 127429 ▁کردم 127425 ▁ряд 127424 ▁Wild 127423 ଣ୍ଟ 127416 ▁별 127411 עניין 127403 palvel 127396 jemo 127386 ▁Pret 127386 ▁maay 127385 арма 127383 وث 127382 ವನ 127379 tiri 127378 ▁എന്നാല് 127377 umenta 127374 ▁menet 127373 pär 127369 ▁କିନ୍ତୁ 127369 ovne 127365 ▁چنین 127362 ▁superficie 127355 blick 127353 вало 127344 ▁Kri 127344 ▁erag 127343 ▁salt 127340 ▁ટી 127338 ▁Ero 127337 ▁الخط 127334 ▁prazo 127333 장은 127323 cane 127321 عبة 127319 ▁Pār 127319 зина 127318 ▁گیرد 127315 ▁אפילו 127311 бр 127310 īst 127309 ▁proš 127309 ▁თემ 127309 ▁ziliz 127304 ikhul 127300 ▁puud 127296 isst 127295 ▁vatten 127293 想到 127287 НІ 127281 ▁вариант 127280 ▁продукт 127279 മില്ല 127274 ▁ƴ 127272 ▁mediu 127267 ポイント 127265 답 127263 ▁принима 127262 ▁فال 127261 ▁aset 127253 ▁잡 127252 ເທ 127251 ▁stanie 127245 ələrdə 127244 ▁Chỉ 127242 vald 127241 状 127236 ;) 127232 PN 127230 邦 127227 ▁yab 127226 앙 127226 ▁godz 127222 ▁rural 127219 ▁קור 127218 vyk 127212 ▁змо 127212 特色 127202 ebet 127196 ▁Frau 127194 ▁ਝ 127186 为什么 127186 ▁Hiện 127184 naden 127177 ▁instala 127177 ▁МВ 127172 ▁ujar 127169 udur 127168 ▁محصولات 127168 ▁सग 127167 ▁جلد 127165 ▁exemplu 127163 ▁omen 127162 ▁Америка 127161 ▁organism 127158 :46 127152 体系 127150 ▁ukuphila 127148 ▁книга 127145 ▁ukuya 127143 siyasi 127142 ukus 127137 うち 127137 ▁روابط 127136 ▁Την 127135 ▁இருக்க 127135 1.0 127134 ▁בער 127131 ▁phi 127124 शु 127122 ▁marta 127121 imenta 127117 ▁Milano 127115 љен 127114 ээн 127111 ▁τότε 127110 ▁արժ 127106 ▁Revolu 127103 tifica 127102 力量 127102 ierten 127099 eritud 127098 ▁retour 127095 ▁check 127094 固 127093 ଦି 127091 okset 127086 ▁Raha 127086 ément 127081 ▁معن 127077 /15 127076 ▁birçok 127075 ამო 127070 ▁शिव 127068 ▁stā 127065 ▁گیا۔ 127062 ▁kaM 127057 ▁начи 127053 Zulu 127049 ▁мог 127048 ynd 127045 ▁Além 127043 ▁producción 127043 面的 127043 chez 127035 સર 127032 ▁espanh 127024 특 127022 ▁იმის 127020 ▁amag 127018 тные 127014 ▁khóa 127005 ▁langkah 127005 ▁култура 127003 த்திற்கு 127002 ukhul 127001 ▁როცა 127001 ոթ 127000 োট 126999 ▁lễ 126996 一年 126993 зей 126983 ٹری 126982 ▁touch 126982 ▁axborot 126976 ▁Tə 126974 ▁ಭಾಗ 126973 ▁гэты 126967 ▁програ 126964 cn 126962 ANDA 126961 ▁کئی 126955 ▁previs 126954 ▁baph 126948 ▁phot 126946 ▁üt 126946 шата 126944 ადგ 126943 த்திர 126942 ៀប 126941 ▁Чтобы 126938 ▁água 126933 чої 126932 ▁Vald 126931 ▁المللی 126927 ওয়া 126925 هل 126923 賞 126919 ▁უფ 126915 чений 126908 ▁обрабо 126908 ্ম 126906 ▁क्लिक 126906 ▁Mig 126904 ▁recl 126902 quier 126900 ▁സോ 126893 udis 126891 भन्दा 126891 ப்பட்டு 126889 ந்திர 126888 වල් 126888 ▁Српске 126888 éz 126884 ▁cale 126883 ▁îm 126883 ▁월 126883 ▁dank 126880 ▁keçirilib 126879 ▁ekolog 126875 рҙар 126872 ▁Castro 126872 ▁incorpor 126871 ▁XIV 126867 ▁metabol 126867 ▁progress 126867 illon 126863 ▁usług 126860 下来 126854 دف 126851 ämme 126847 ▁renov 126846 ▁Sản 126844 ▁hivat 126844 ▁Сон 126842 ਿਆਨ 126841 ▁החברה 126838 ▁знача 126835 贸易 126835 ▁feliz 126834 ▁Inv 126833 秀 126833 ▁රටේ 126832 ಟ್ರ 126831 ▁telev 126822 ▁Kosovë 126817 ຍາ 126815 洛 126815 ിലേക്ക് 126807 ▁բեր 126804 mei 126803 ▁ଘର 126802 สร 126796 ▁oral 126795 ▁തൊ 126795 ▁ребенка 126786 جيل 126785 ▁penger 126783 lala 126780 ▁ұста 126780 ▁판 126779 ნო 126778 ▁Chile 126776 [6] 126775 ▁cunha 126767 ▁fito 126766 ເນ 126765 ▁счет 126763 лении 126760 ▁eneo 126756 乱 126753 峰 126751 ▁135 126739 ▁dgn 126738 ände 126733 ▁поля 126727 ▁Akan 126726 ▁exterior 126723 ▁CAR 126719 ▁falt 126718 ▁sitter 126718 גם 126717 ▁الاحت 126716 ▁אופ 126714 コミ 126714 ▁буюу 126713 ▁сфе 126709 ▁בחי 126705 nění 126703 ገኝ 126701 ▁Rakyat 126700 िता 126698 ▁ನಾ 126698 λογία 126697 ▁interi 126697 tro 126695 прия 126692 ▁સહ 126691 ▁Şi 126688 کھ 126685 ▁xunu 126685 –19 126684 ▁גרויס 126684 ස්ට් 126683 ▁lokasi 126683 ▁تعليم 126681 वासी 126671 ▁konsep 126671 érer 126670 ಲಾಗಿದೆ 126670 ▁белем 126669 10] 126666 ▁своим 126664 ▁wichteg 126663 ូម 126659 ▁olay 126656 ▁informe 126653 ഞ്ഞ് 126648 ▁අම් 126647 ▁възможно 126645 ▁asnjë 126643 ▁paraan 126643 ▁ຫລ 126642 ▁Fé 126639 ▁बड़े 126635 ▁വ്യ 126635 ▁šajā 126629 ▁prič 126626 ንተ 126621 дой 126620 ზის 126606 ▁tiyang 126605 тэр 126604 ítja 126603 ໂຄ 126600 ▁moka 126597 yvät 126587 मुख 126587 ▁πέ 126587 ▁இந்தியா 126587 ແບບ 126577 არდა 126576 ▁edes 126573 ▁resposta 126573 ଯାଇଛି 126567 mart 126566 ▁أب 126565 ้าร 126562 olio 126561 ▁ሴ 126557 ណ៍ 126549 enaar 126546 正常 126539 ▁hadir 126538 ബു 126530 ▁ဘယ္ 126525 imbo 126523 ▁sette 126522 ▁dấu 126519 ▁אידישע 126517 ▁täna 126510 עור 126507 əlidir 126502 ▁международ 126502 ▁Magufuli 126499 ▁אתר 126495 津 126490 त्त 126484 ▁Tie 126484 ▁બાદ 126476 année 126472 Ro 126471 xh 126471 ▁Hung 126469 ▁følgende 126467 ▁küs 126465 ғат 126458 луу 126456 ▁Möglichkeit 126454 ▁près 126451 ▁sny 126451 ńczy 126449 ▁fortal 126443 ындағы 126427 联系 126426 istaan 126425 ▁mājas 126420 ▁ҡулланы 126419 ▁அனு 126416 ▁jį 126411 ▁beiden 126410 荷 126404 ေတြကို 126402 adeen 126391 ▁yethu 126389 ▁хурал 126379 ▁יהיה 126375 ▁значение 126372 үүдийн 126369 ▁влия 126369 ▁lavar 126366 rets 126356 ועל 126354 kező 126344 ▁famille 126342 िम 126340 ▁stimul 126333 ▁lus 126331 ▁القد 126330 ▁कंपनी 126329 ▁mestu 126328 őd 126325 ▁wëll 126321 おり 126321 ▁lua 126318 toon 126311 ▁muligt 126306 ▁מיין 126304 ▁съще 126293 核心 126290 ▁იც 126287 ▁ummata 126286 пута 126285 ▁fia 126283 ▁댓글 126280 odan 126278 ▁protected 126278 বেন 126276 ▁음 126275 dið 126274 ત્ય 126270 ▁convertir 126265 жня 126255 ▁tomb 126251 ▁Kerja 126243 ▁ҡуйы 126241 ▁ùr 126239 ▁makap 126234 ▁weiß 126232 ▁Kend 126231 hari 126230 ▁אָדער 126224 tü 126222 ộc 126222 ▁piemē 126220 ▁фирма 126217 ▁ব্যা 126214 ▁lãnh 126213 ▁तपाईं 126213 ▁función 126208 ▁xarici 126208 診 126206 ▁آینده 126200 と考え 126191 сцю 126189 ▁inhliziyo 126186 ▁ေရ 126182 መው 126180 :38 126178 ▁Quan 126178 文件 126178 غو 126177 стат 126176 03 126170 ycznej 126167 ▁bezañ 126166 ▁геро 126166 ▁vek 126164 ▁ايس 126160 ▁Netz 126155 могу 126154 ariak 126152 ▁apare 126152 ▁бен 126145 Զ 126141 massa 126139 ਼ 126139 ▁ادب 126138 ▁varia 126134 hlak 126131 ▁ändå 126131 ▁اختلاف 126131 ▁davvero 126130 ବର 126129 apati 126126 ▁отсут 126125 ▁приват 126125 ▁ndryshme 126123 etika 126119 ▁ايران 126118 jk 126116 ▁reli 126115 isyen 126114 tighet 126111 ▁Ghe 126107 ▁након 126102 ▁অনু 126098 ▁líne 126093 ຊີ 126092 ಹ್ 126091 ໃຫມ່ 126090 ▁máximo 126086 ▁помощи 126086 ▁नवीन 126082 пози 126072 ▁Agu 126072 ▁بب 126072 ▁medier 126070 ▁उपचार 126070 ▁ताल 126067 វត្ត 126064 ▁Нови 126061 ▁ພາ 126061 ▁vlastní 126058 ▁spir 126055 ▁gaaf 126054 ▁გაი 126049 ▁неболь 126047 ▁сторо 126047 ▁vody 126046 ีน 126044 endus 126041 ിവ 126041 ▁کت 126038 ▁χει 126033 클 126030 कल 126028 irus 126027 народно 126027 ▁Sound 126024 ከላ 126020 лый 126011 شنل 126010 ▁poslan 125997 ...). 125996 ▁اٿ 125995 ▁ardından 125989 ▁kabla 125988 ▁pravice 125986 usias 125980 ▁нават 125975 anggih 125968 ƙ 125958 idus 125953 ▁zullen 125952 шых 125950 複 125944 හන් 125943 ▁proven 125940 ▁බ්ලොග් 125940 摩 125936 ønne 125934 ▁Jamhuri 125934 ▁Мне 125933 უტ 125929 خي 125925 ▁enkele 125925 ▁joy 125920 ▁работни 125917 ugcina 125912 ഭാ 125902 ▁środ 125902 ▁standart 125897 ▁difficile 125896 ▁क्यों 125895 ողների 125892 ່າງ 125891 ▁terre 125888 ▁اعتماد 125887 timit 125874 ყვარ 125874 ▁парк 125872 ▁protagonista 125870 ▁финан 125869 ▁simb 125867 ▁stór 125865 ▁LU 125864 ▁mī 125856 жды 125855 ▁хүмүүс 125852 oport 125850 ▁уг 125848 ▁haver 125847 mr 125843 isija 125833 ▁yase 125831 ▁Απ 125830 ակարգ 125826 ▁резултат 125826 ▁וועגן 125826 ▁(25 125825 ▁πολλά 125822 ▁gik 125821 ▁wajib 125820 ▁dodatk 125817 ▁අයි 125817 òd 125816 ບັນດາ 125815 ΜΕ 125812 ▁Agora 125810 一番 125810 Һ 125801 發展 125800 arni 125796 ▁akiwa 125794 ▁katalog 125790 ੰਬਰ 125787 ▁მიღ 125787 гэр 125786 ▁Federaalka 125785 ▁நே 125781 yso 125780 СКА 125776 ▁उठा 125775 ▁නායක 125775 ▁şirket 125771 ▁kvinn 125769 ▁สามารถ 125769 ipo 125767 ▁служ 125766 ▁vant 125765 ▁power 125764 ▁попро 125763 :03 125756 nä 125754 ▁собстве 125754 ▁lif 125753 ▁எனக்கு 125753 asjonen 125751 зка 125748 ▁Basa 125748 elho 125747 tség 125747 ▁വരെ 125744 ▁чы 125743 ▁гишүүн 125737 ▁costru 125735 ▁abu 125729 sters 125728 ਕਿ 125724 ▁Messenger 125722 ▁Запо 125720 ර්ශ 125719 ▁izin 125718 ീസ് 125713 ври 125703 ເຫັນ 125698 ▁ຄໍາ 125696 ське 125693 leist 125688 ▁ezek 125675 ▁ڊاڪٽر 125675 ▁gesp 125668 ivati 125666 òi 125657 ▁ziyaret 125657 ▁شدید 125655 ▁duwan 125654 ▁foreld 125650 ▁človek 125650 чыў 125647 ▁그러나 125646 ▁Тем 125644 ▁Dic 125643 θεν 125639 ▁ابت 125639 ▁inj 125636 ▁loop 125632 ▁phezu 125629 σκ 125625 țiile 125623 ▁tract 125618 ικούς 125617 точно 125617 ▁mache 125613 informa 125610 ▁kalawan 125609 ▁Bundan 125608 ▁makat 125608 ugur 125605 ▁komunika 125605 ▁Ā 125605 ▁плат 125604 ▁Alban 125602 ▁repres 125601 ▁thabhairt 125601 ▁monument 125599 ▁khăn 125596 ▁ഖ 125591 ▁kerk 125589 fold 125588 ersa 125586 μών 125585 ▁ወይ 125580 ▁pribli 125579 ▁przysz 125579 uktur 125578 ▁taraf 125577 ▁тұлға 125577 머 125576 ▁ร้าน 125572 inget 125566 を使 125559 ஜி 125554 ▁Videos 125554 ▁firenena 125551 ▁khảo 125548 ▁reprezenta 125543 ▁toks 125541 ▁Mall 125540 ▁Tartu 125540 தில் 125537 ▁פיל 125537 şit 125530 ▁Tras 125529 atro 125524 ▁tredje 125518 θεια 125514 нікі 125505 ▁مې 125504 лее 125503 мено 125503 ▁1981 125502 nością 125501 ▁ferð 125499 ▁sikhathi 125498 արանի 125492 ▁السبت 125490 データ 125488 ▁صلی 125487 ってしまう 125478 ರವ 125477 ▁valami 125474 ▁menyer 125472 ▁Sos 125470 ciendo 125468 ▁risc 125467 ▁Yakhe 125462 よりも 125460 ▁त्यांनी 125459 ▁κόσμο 125455 ▁izved 125452 ▁Холбоо 125452 ▁ume 125438 ▁페 125435 ▁menyel 125433 ໄລ 125432 ▁обје 125432 ेंट 125430 deles 125426 ិប 125423 ▁ફે 125414 ▁změ 125407 ▁경제 125403 ▁ගො 125402 ▁omul 125400 ▁edə 125399 ▁mengen 125393 emiz 125392 โย 125391 ▁váš 125387 ▁ите 125381 antza 125377 右 125377 ▁ungdom 125376 opia 125375 ▁پوست 125375 ▁haala 125370 ٹھ 125369 ▁Leist 125367 ▁Bilisummaa 125361 ▁קול 125357 ▁எப்படி 125357 ▁Aquí 125354 ▁Tage 125353 ▁mate 125353 naf 125348 នី 125343 ▁Vaše 125342 ▁سالن 125340 ▁layanan 125334 ▁داری 125334 ▁ਹੋਇਆ 125331 ▁معامل 125329 ▁asturianu 125328 ▁pozwala 125328 ғни 125326 ▁үҙгәр 125324 kira 125320 ▁Teknik 125314 ▁нарко 125314 ▁circu 125313 ٹو 125302 פש 125299 त्ति 125295 ▁хувьд 125295 ▁AQSh 125292 ▁Riv 125291 ▁вопро 125290 ▁canvi 125289 nader 125282 ▁начала 125282 ▁돌 125282 зат 125280 ույց 125267 ચાર 125260 ධාර 125260 ▁좀 125258 ▁muni 125256 ▁filma 125252 ▁Gul 125250 ▁જીવન 125245 ticamente 125239 ▁vlak 125239 haal 125238 ▁edilen 125238 ▁sona 125235 lau 125233 muje 125230 ▁saist 125230 냐 125229 imir 125228 ▁उनकी 125228 ▁1974 125225 ▁دعا 125216 ▁Pakistan 125215 ▁корпу 125212 ▁МО 125207 ▁Дими 125200 ▁diha 125197 mung 125196 ட்டில் 125195 ▁ditugu 125194 ▁preciso 125190 ابي 125186 ▁toliko 125180 ▁niiden 125178 ▁Ил 125176 AHA 125174 ದಲ್ಲ 125174 ▁Hər 125174 ▁جہاں 125169 ▁овог 125164 ၿမိဳ႕ 125163 נגע 125161 ុទ្ធ 125160 ▁Ги 125159 ákat 125158 ▁ọmọ 125156 ▁bora 125155 ▁розпові 125155 nejšie 125153 рэй 125151 ▁šal 125151 ▁យក 125151 なければ 125151 ריות 125149 èir 125145 зал 125144 इट 125142 ▁порно 125141 স্ট 125140 voer 125137 skil 125134 ▁oant 125133 ▁конце 125133 ▁יד 125132 ▁Cé 125130 ▁chăm 125127 คุณภาพ 125119 ▁stadion 125119 ▁уларҙы 125115 糖 125115 ▁трудо 125114 дыр 125113 ▁шара 125101 эгч 125098 دې 125098 ▁siglo 125094 ვანი 125093 ▁vente 125093 ▁தூ 125093 ▁eppes 125092 ▁vietas 125085 χαν 125082 ▁ამის 125079 ▁Asturi 125074 ▁besonders 125073 ▁Ју 125069 ન્દ્ર 125068 "; 125067 ▁Mountain 125064 ▁asina 125062 ására 125061 ▁pell 125061 ▁semaine 125056 ສໍາ 125055 čnega 125053 ▁законо 125053 ▁contacto 125050 ▁محصول 125050 ▁จึง 125047 ▁держав 125045 ▁zami 125041 ▁कम्पनी 125035 ulag 125034 ▁Arti 125033 ▁vive 125030 ▁mès 125023 ▁표 125023 ▁ទៀត 125022 ▁Beispiel 125019 ницима 125017 ▁Catal 125017 ំន 125015 ▁kilom 125012 ▁mbele 125011 eon 125004 اك 125004 ▁նման 125001 ▁tipe 124999 dial 124996 ▁госпо 124994 ▁Kada 124991 ▁dues 124977 ▁cach 124975 ▁kumpulan 124963 ▁remont 124962 ▁εί 124960 ▁პროგრამ 124957 ▁адзін 124951 දන 124950 ดับ 124950 just 124949 ▁läsa 124948 ▁porr 124948 ▁lese 124940 จํานวน 124936 ▁ទឹក 124934 ჩე 124929 ▁عص 124926 ▁Enn 124924 ▁поза 124920 ▁arag 124916 حية 124914 ▁gamla 124911 ▁неща 124909 times 124908 ंदी 124906 ▁مث 124905 ▁kraja 124902 जू 124900 ▁Gesellschaft 124889 ▁toeg 124885 ــــ 124884 もちろん 124882 ▁Investi 124880 ▁Konf 124878 ▁aniq 124878 фат 124875 टन 124875 ▁உத 124874 wayelekile 124869 ustav 124867 រូប 124867 ▁зни 124865 ▁გოგ 124862 cią 124856 ▁Energie 124856 ▁kvart 124856 ▁xur 124856 istin 124855 ▁kweli 124853 नो 124852 ▁1,8 124852 ▁طبي 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123166 ▁веќе 123166 ▁שירות 123162 ଚାର 123160 ▁păr 123159 Porn 123157 ട്ടില്ല 123157 utare 123156 ▁1973 123152 ▁тәрбие 123152 skrif 123151 கோ 123148 ▁गांधी 123148 媽 123148 付け 123146 ▁fólk 123144 ▁эхэл 123143 ▁descub 123140 ခင္ 123136 коло 123134 ▁lase 123134 ovna 123129 ریب 123128 ▁alimentos 123128 ણો 123126 ▁vrt 123120 sey 123117 ▁Asa 123111 ▁brak 123107 сур 123106 ▁clasi 123106 ▁middel 123102 률 123101 ▁=> 123097 kei 123095 ▁nala 123095 ▁alternativa 123094 كز 123093 шено 123092 ▁nhóm 123090 ▁chóng 123088 這些 123088 ▁lyck 123077 ▁pół 123075 ▁Personen 123074 joit 123063 ▁truc 123063 ▁deenen 123055 ▁gadget 123055 ▁pami 123055 ▁በደ 123054 ريا 123052 ုိ႔ 123050 娘 123050 ▁준비 123048 ▁დაკ 123045 قيقة 123043 ▁እንዴት 123033 બર 123030 ▁Janeiro 123027 ▁страната 123027 ▁تعلیم 123025 ▁bohat 123020 їз 123018 orti 123016 ▁niej 123016 ▁келген 123016 工业 123013 ▁୭ 123012 ▁pertandingan 123008 ர்களின் 123004 ▁төркөм 123004 犯 123002 ▁ရက္ 123000 laf 122994 nama 122993 ▁всю 122993 ▁еко 122988 wik 122985 oslav 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120028 ▁йылғы 120028 ▁swoją 120025 ▁મી 120022 ▁කෝ 120019 ▁ڳاله 120018 ▁ძალ 120016 高い 120014 ▁hjelp 120010 enei 120004 ▁Tolosa 120002 Por 120001 പ്പോൾ 120001 ▁ಮಾತ್ರ 120000 ▁एमाले 119999 фы 119998 ▁Där 119998 kooda 119997 ▁lộ 119997 ▁пошу 119995 โครงการ 119993 אם 119991 ▁peaks 119989 ▁հոդված 119987 ರಣ 119983 ▁Jau 119978 ▁svēt 119975 াইল 119971 ▁வய 119970 ▁انس 119965 ativu 119963 ▁Frauen 119963 sme 119960 ▁Sè 119958 Ū 119956 ▁mbësht 119956 ▁verej 119956 够 119953 ልጣ 119951 vaar 119947 isine 119945 ▁egne 119940 タイ 119939 ▁tudom 119937 ▁ტრ 119937 ▁ہزار 119936 sht 119934 DN 119914 ▁autori 119913 ▁אנדערע 119913 ▁उपा 119913 odom 119908 ▁Uit 119907 ▁لد 119902 ▁tätä 119894 ▁Suomessa 119893 ▁मेरो 119893 ▁Türkiy 119889 ulm 119886 ády 119885 mans 119877 ▁buruzko 119876 терін 119875 ခြင်း 119872 が出 119870 ▁приста 119868 aysan 119867 ▁Imam 119867 ▁jual 119867 ▁ฝ 119866 ▁meira 119861 ▁الأحد 119856 ▁finna 119852 ▁20:00 119848 ▁국가 119848 ▁വര 119846 기업 119844 ▁працы 119841 ▁የሚል 119834 ▁істо 119833 ▁говорит 119832 ▁Polski 119831 ▁บ้าน 119831 抵 119830 ▁Hj 119827 ▁function 119824 шыя 119823 ▁обязательно 119823 ▁vremena 119821 ▁библиоте 119817 ▁Telef 119811 ▁bekommen 119809 ▁여러 119809 גת 119806 ▁رأ 119806 taget 119804 ▁직접 119803 ▁чалавек 119798 惊 119796 ўнага 119795 ▁filosof 119795 ▁мед 119794 ▁શ્રી 119789 人も 119789 ▁уларҙың 119784 출장샵 119783 ▁Mustafa 119780 ▁wiem 119775 ▁έχουμε 119774 ▁राह 119774 ▁компози 119768 ặc 119767 ▁Cle 119766 ▁reuni 119764 ▁যায় 119763 摄 119763 ▁чым 119756 ▁ارب 119754 ▁Somalia 119751 ▁економ 119750 ▁bă 119749 ▁varð 119747 ▁sèvis 119745 untas 119741 vení 119741 áty 119741 ▁mercato 119741 umun 119740 ▁بڑھ 119734 echt 119729 ▁yolu 119729 ▁kola 119724 ▁syl 119720 ▁apel 119712 ագետ 119711 kasadpang 119707 ▁დან 119706 ikoak 119705 irit 119705 ▁oks 119705 avientu 119703 anyak 119701 ▁αφ 119698 ▁fez 119695 教学 119692 ետր 119691 ▁mañana 119683 ▁урында 119683 ▁klju 119681 ▁biais 119679 ▁nationale 119677 പ്പെ 119675 ▁силу 119675 лая 119673 ▁управля 119673 ▁дви 119672 ▁خواتین 119670 ထဲ 119667 ▁dane 119666 ▁ଆଇ 119662 ätter 119660 boll 119658 ▁Республики 119658 ▁କ୍ଷ 119657 ▁Travel 119655 ▁yüks 119651 jate 119650 йдет 119650 ٽو 119650 ▁kommune 119647 ▁வேலை 119646 dóttir 119644 ▁económica 119644 დას 119643 ▁bakhe 119639 йә 119638 .5 119637 ونې 119637 εκ 119636 肯定 119634 जय 119633 ▁plā 119633 ▁hire 119631 ▁አር 119631 قية 119630 ▁дос 119629 ▁знам 119629 ▁хий 119625 ▁caf 119624 һи 119621 ▁wsp 119621 左 119620 ▁tuot 119618 პორ 119614 ▁heldur 119613 ▁woj 119608 ▁NGA 119598 ▁Ran 119598 мую 119596 горе 119593 ▁peti 119592 дығы 119588 ▁маги 119588 انس 119587 inici 119585 ▁bandar 119579 ▁információ 119579 ▁Daug 119577 varan 119576 ാനുള്ള 119575 综合 119575 ್ಯೂ 119568 ภัย 119568 ច្រើន 119567 历 119566 ätze 119565 บบ 119564 hong 119561 ▁PK 119552 तार 119551 ▁curi 119548 ▁тэмц 119548 חון 119547 ▁прибли 119547 жне 119546 ▁जाती 119546 лаў 119545 ഐ 119545 ▁jang 119543 ▁коме 119543 īd 119540 kai 119538 ▁արդյուն 119538 ▁cabeza 119536 ▁mengikuti 119532 ▁발생 119532 ▁nhẹ 119529 ▁agak 119527 ▁Cristo 119526 ▁xineru 119526 edas 119522 ▁posamez 119522 왔 119521 nā 119518 ▁یافت 119516 ▁exper 119515 ▁табылады 119512 ▁മന 119511 यल 119509 ▁निर 119509 пъл 119508 ▁coles 119508 cang 119504 ▁shume 119504 ▁echip 119503 elv 119499 ▁cười 119498 odata 119497 ▁euskal 119496 ทาน 119495 ▁דור 119494 ▁أبو 119492 ▁nyug 119488 ларҙың 119486 :58 119484 ോട് 119481 ▁sumar 119481 ▁nínú 119478 ▁meminta 119475 ▁Mitarbeiter 119470 ảnh 119467 ▁гээд 119466 ခ်င္း 119462 ▁опре 119461 თქ 119458 দে 119456 ▁î 119452 ▁Jā 119449 сқан 119444 ▁بڻ 119442 ▁jasa 119441 ▁serveis 119439 ikom 119437 нери 119436 ▁dəyiş 119436 ▁Харин 119434 ▁नेतृत्व 119434 ▁escola 119430 ເຖິງ 119424 ックス 119424 ▁Bereich 119423 ήσει 119420 ဥ 119420 ศรี 119419 ▁ruj 119416 ▁ಲೈ 119408 ່ວ 119406 աշրջ 119403 ▁געווא 119402 وقف 119395 ាគ 119395 ಿಸಿದ್ದಾರೆ 119393 者的 119392 žiūr 119384 ристо 119384 ▁hlas 119379 ▁Naidheachdan 119376 ッチ 119376 ▁забе 119375 ▁hvort 119374 리고 119374 ące 119368 бър 119368 แต 119367 ▁flok 119367 person 119363 ▁slip 119357 ▁სახელმწიფო 119357 麗 119355 aturan 119348 radi 119347 ▁trés 119341 čine 119337 ▁фер 119337 ▁Plaz 119336 ▁driver 119336 ▁cio 119334 ▁możliwość 119333 ▁dugo 119332 ▁tengok 119332 ▁Situs 119327 šanos 119322 ▁история 119320 ▁කට 119318 ජා 119317 ▁закры 119317 өөр 119316 ▁Bảo 119314 maydi 119308 ▁如果 119300 lesia 119299 ▁piedāvāj 119297 ▁ছিল 119293 merk 119291 ▁Beibele 119287 aldeko 119286 宮 119282 ource 119277 ▁fiatal 119274 ▁វិទ្យុ 119274 ayin 119266 inb 119251 ▁ABC 119251 ▁ვე 119248 ▁biaya 119243 ▁හේතුව 119241 ▁склад 119240 ▁earste 119235 insuku 119232 riften 119232 ▁சொன்ன 119231 штва 119229 ▁बताया 119229 โพ 119226 éan 119225 ▁רש 119223 ▁Wee 119221 ▁objet 119218 כתב 119217 дити 119216 μέν 119214 ▁සමඟ 119213 ▁výsled 119211 ▁fisc 119210 ▁batter 119205 ▁kuras 119202 ▁săn 119199 ▁ministro 119198 aviy 119196 čić 119196 ▁କାମ 119196 ▁assez 119194 ▁probab 119193 ukaan 119191 ▁комиссия 119190 ▁വളരെ 119190 טת 119187 EGA 119182 ▁ক্র 119181 abrik 119178 ▁koš 119178 ▁obliga 119178 ▁Jin 119176 ▁sesi 119171 ružen 119169 ▁සතු 119166 oras 119162 كار 119158 ünə 119157 ▁liom 119155 ▁گوشی 119154 ▁օգտագործ 119153 اتے 119148 ▁neke 119148 sna 119146 ଗୁ 119140 一点 119139 ҙер 119136 የር 119135 ▁گونه 119133 ▁4-5 119130 utiliz 119129 ▁دکتر 119128 ▁80- 119125 ▁następ 119116 ▁میکن 119115 ▁Regul 119113 istar 119112 ākais 119108 ▁ගොඩ 119106 จีน 119103 ▁حوالے 119089 ▁chéad 119087 レー 119086 ▁defini 119084 енә 119076 ▁tavo 119076 ▁ሳይሆን 119074 ▁Pää 119068 ▁ຂ່າວ 119066 arius 119064 ▁निय 119061 نګ 119045 iteten 119041 ιοι 119041 ▁व्यक्त 119041 ແລ້ວ 119039 ▁опы 119037 culos 119035 ▁aprob 119033 ▁ላ 119033 acu 119032 ▁подтвер 119031 ยาย 119030 ▁ነበር፡፡ 119030 ാനം 119027 ▁pitit 119026 ▁අධික 119024 ▁വിശ 119022 haba 119016 ▁Contin 119015 ▁ánh 119009 impan 119008 ▁据 119008 Õ 119007 dering 119006 ジャ 119001 bene 118998 ▁خیال 118997 ▁vilja 118996 ▁namn 118993 ▁Pub 118992 tól 118988 asını 118986 ▁ອອກ 118982 ▁איין 118980 ▁Prime 118979 unte 118978 ▁sû 118975 做好 118975 ▁ٿيل 118973 ▁ervaar 118972 ▁istedi 118969 ▁уви 118965 ▁ಹೆಚ್ಚ 118965 ▁compag 118963 estig 118961 ▁čr 118961 ▁सडक 118956 čkoj 118954 ▁йөр 118947 ▁ਅਗ 118947 ▁aplicación 118943 ▁없습니다 118938 فو 118933 ▁dibu 118933 ▁rápido 118933 ▁Woche 118931 稱 118927 ментар 118920 conom 118919 ▁greit 118902 ▁իշխան 118896 ▁भाजपा 118896 ▁banka 118895 ▁oči 118888 jocht 118887 ಾಲಯ 118883 ▁پوليس 118883 ▁وارن 118878 ▁vych 118871 ▁навчання 118869 ebwa 118867 あって 118867 消费 118861 tego 118859 manna 118852 ▁poderá 118849 σό 118848 벽 118848 രിയ 118846 ▁Автор 118844 ▁figyel 118843 逃 118843 izion 118841 ▁oes 118838 ▁здійсню 118836 ▁ڪندا 118835 ▁വയ 118835 unner 118834 אס 118832 舒 118832 ▁tekint 118831 ורת 118830 ▁trener 118830 ▁разу 118830 錯 118829 dire 118828 ্যান্ড 118828 ▁наи 118825 stein 118823 xicu 118821 ▁በየ 118818 rete 118816 oksi 118812 懂 118811 ▁подгот 118806 ларҙы 118803 ▁만들어 118803 roš 118787 ▁Krie 118783 ГИ 118781 ▁benz 118781 ▁zake 118781 ▁Trend 118779 ▁Faz 118777 ້ອນ 118774 ▁artist 118774 ▁betro 118774 ರಾಜ 118770 ώσει 118768 ▁کلیک 118764 ▁Új 118763 ▁dorm 118761 ▁жалпы 118761 ▁typer 118745 ▁Lwakhe 118744 葉 118737 ▁enten 118736 ▁berd 118734 ▁আমরা 118731 ▁hek 118721 ▁pc 118721 ▁препара 118715 ▁ځانګ 118714 വരെ 118713 ।’ 118709 ▁Jad 118704 ▁ہاتھ 118700 élt 118696 dies 118693 変更 118689 ▁අඩු 118686 vim 118679 file 118678 赤 118676 ෙන්නේ 118675 সহ 118672 ▁каля 118672 ▁stöd 118671 ▁манай 118670 طلب 118667 ତ୍ତି 118666 osy 118662 ▁abb 118659 ▁cải 118656 ▁gjelder 118655 ▁೧ 118655 entrada 118651 irəm 118647 нє 118647 ▁அரச 118647 ▁αδ 118646 ▁коментар 118646 ▁persoan 118640 ▁bantuan 118636 istenza 118632 vd 118630 ▁соответству 118629 ▁societat 118624 ▁iznos 118618 ▁ຕ່າງ 118616 iav 118614 ▁գլխավոր 118607 ▁intra 118605 ਮਾਂ 118602 ▁näky 118602 ủng 118597 ▁εται 118597 เฉ 118595 ▁беспла 118595 /16 118592 ہا 118591 ▁БН 118588 ▁ohjel 118586 ▁όσ 118581 fti 118580 ▁පිළිබඳ 118579 ▁Seperti 118577 ▁commun 118577 stant 118576 ваше 118573 klas 118561 ėse 118559 ▁etim 118558 ▁merci 118554 了一个 118551 ата 118550 ▁institut 118550 ▁εργα 118549 ▁habitantes 118546 ▁její 118545 ▁maç 118541 ▁секс 118536 ಗರ 118534 ▁suất 118530 ▁Ради 118528 ▁desenvol 118525 amii 118524 父母 118518 ▁calend 118514 さんが 118514 ▁обаве 118512 ▁asigur 118507 ▁ίδια 118507 ▁දේශපාලන 118507 ▁၄ 118504 ▁hakkunde 118503 OND 118502 usay 118502 ▁Strateg 118502 aliwa 118496 ▁τά 118495 jab 118493 ▁1948 118491 ▁μεγάλη 118489 ▁няка 118489 ▁resurs 118483 ▁құры 118482 တိုင္း 118480 ▁készül 118479 ▁Lena 118477 ▁जित 118477 ▁העולם 118474 ▁Rela 118473 ▁bucur 118472 ieniu 118470 ▁gomme 118470 ▁elan 118468 年の 118465 ропо 118462 illet 118461 ▁这 118457 وسط 118455 lane 118447 jende 118446 anaf 118445 μές 118444 ezaji 118440 hev 118438 準備 118438 ▁napi 118433 ໃດ 118428 ▁akty 118427 ▁nossos 118427 ▁കയ 118426 िंग 118425 ▁deas 118425 ▁sprav 118425 ▁demost 118424 ▁आफू 118424 ▁Đà 118419 אָן 118418 ▁ίδ 118418 自動 118412 ΑΙ 118397 New 118391 ▁koti 118386 ▁κάπο 118384 ▁auð 118381 ▁Laura 118378 ▁처음 118374 ခန္း 118372 ▁ішінде 118370 ▁झा 118368 ເຂດ 118367 ummer 118358 Jo 118356 ▁अंक 118356 በው 118354 ذي 118349 ▁Musta 118349 ▁Меди 118347 fst 118336 свет 118332 ▁Lle 118324 ▁kancel 118323 plex 118322 sif 118322 дад 118320 ▁ΠΑ 118320 ▁ஆர 118319 ▁satış 118316 ▁Hien 118315 ▁печа 118315 spel 118314 ttes 118312 фера 118309 ▁ክፍል 118309 ▁школи 118306 ▁stri 118303 ▁società 118296 ▁instit 118293 ▁máu 118292 дыг 118290 ▁electrónico 118287 śnie 118284 ▁постоян 118284 emn 118283 បរ 118282 ▁Quer 118281 ▁açık 118281 ▁kilomet 118281 ▁थो 118281 ▁milik 118280 /2012 118278 ▁Tamién 118275 now 118274 ▁epis 118273 ובות 118271 ട്ടും 118271 ▁ଓଡ଼ିଶା 118264 ▁taman 118263 ▁دری 118261 ▁yanında 118259 ▁trening 118256 ▁прямо 118251 土地 118250 ▁tana 118249 团队 118249 ▁Entr 118246 ▁მათი 118242 ▁pomo 118238 ▁ඊට 118234 גיש 118231 ▁الشعب 118229 ▁теат 118228 日常 118225 ▁मग 118220 ▁उद्योग 118214 ▁hre 118208 ▁самоу 118205 ▁ଆସି 118204 済 118201 ▁Quand 118199 ▁baay 118199 ireacht 118198 ▁مقرر 118198 ▁spil 118197 ឆ្នោត 118190 ҙәм 118187 мил 118184 ▁toirt 118181 വിധ 118180 ▁مجموع 118177 kta 118176 ▁يؤ 118168 δων 118167 гут 118167 ▁вле 118167 ▁ober 118164 ▁ಸಂಗ 118164 ▁activitats 118161 ណ្ណ 118160 ordan 118155 ферен 118149 ▁huset 118149 農 118149 展示 118148 ▁ನಾವು 118143 ▁algunas 118141 ▁Mož 118140 ▁있었 118140 ▁תע 118134 ▁fada 118133 満 118129 طيع 118128 ▁آهيان 118125 ▁میت 118124 ▁castell 118123 ▁rado 118122 ▁Ман 118119 เย็ด 118117 ▁áður 118117 ▁रन 118116 નિ 118113 ▁azo 118113 ▁kurš 118111 ▁aura 118108 ▁zatr 118108 ▁Phú 118107 ▁ທະ 118107 ▁situat 118104 予定 118102 ▁nifer 118101 vă 118099 ▁өлөш 118099 ▁Tos 118098 ▁kepha 118097 ▁Cuba 118091 ▁problemer 118090 දෙ 118088 ೇನೆ 118086 ▁ٿيندو 118086 ຫຍ 118084 ▁предви 118076 вима 118075 ▁Poc 118073 ▁sigue 118072 usest 118071 ▁nyo 118069 ជូន 118064 ▁хөр 118062 ▁במר 118059 ▁carri 118058 ▁wilt 118053 .] 118050 ▁Ы 118050 េត 118047 ուժ 118039 ▁keg 118035 μένοι 118032 markt 118030 kolo 118028 ▁registra 118028 万円 118027 heti 118014 ▁αί 118014 taba 118012 ամբ 118012 脑 118008 غط 118007 પો 118007 Си 118006 බ් 118005 ▁Vå 118004 inem 118000 ▁نحو 118000 ▁xi 117995 market 117989 您的 117988 atiiv 117979 ▁Thanolo 117979 ▁Pens 117976 ▁geeft 117969 ▁tett 117963 ▁प्रक्रिया 117962 ځي 117961 য়ার 117954 ▁políticas 117950 ▁öll 117949 ▁dokter 117945 督 117942 ဲ႔ 117937 ▁Ym 117937 ▁merkez 117936 ▁dlou 117934 ▁uddann 117931 த்துக்கு 117929 ▁предприятия 117929 やり 117926 ▁onts 117924 ▁mukana 117919 ത്തോ 117917 qlar 117913 ल्प 117913 ▁njo 117913 ▁зүйн 117909 iltä 117908 यु 117908 ecom 117907 ujący 117907 ▁visite 117904 ▁някой 117904 რიგ 117899 ▁orgas 117898 豪 117892 පාල 117888 origina 117879 ▁certains 117878 iture 117877 ▁arall 117877 „ 117874 ▁munt 117872 ▁labada 117870 न्त्र 117865 ▁moderna 117864 ▁segond 117863 ▁ауыр 117862 执行 117862 sei 117860 ற்கு 117859 ▁seir 117858 сел 117857 ▁अवसर 117851 ▁Gobolka 117848 ▁බලා 117848 Andriamanitra 117839 ▁szóló 117839 ▁Например 117839 ▁маҡса 117834 ▁நிற 117832 ▁구매 117831 Ал 117830 кае 117819 ▁vielleicht 117818 ▁Evro 117817 ▁ശേഷം 117817 ığımız 117812 ėsi 117811 ▁kandidat 117808 ▁njega 117807 印象 117805 ▁ಬೆಳ 117803 ▁olw 117800 ▁پاره 117800 وبة 117789 үҙ 117786 ่วม 117786 waith 117785 hluk 117784 ▁මහින්ද 117781 ▁Umoja 117776 ร่างกาย 117775 orri 117772 ését 117769 ▁naro 117768 İK 117766 erva 117765 ▁оства 117764 ▁partn 117763 ▁yü 117760 ▁հաշվ 117760 stånd 117757 льки 117757 ▁комму 117755 ▁haddii 117754 atayo 117748 ▁ክልል 117747 jør 117746 ▁Finanz 117739 ▁możemy 117737 ଠି 117735 ▁trots 117735 ▁Президен 117734 êp 117731 ▁مناطق 117731 ▁என்ப 117730 ▁Apro 117722 ▁(198 117721 ▁izahay 117721 Þ 117719 ▁atkl 117718 чном 117715 တွေ 117713 標 117711 ▁ਕੰਮ 117708 ádz 117697 issima 117696 ぼ 117691 ਢ 117687 ▁Gim 117687 ▁εργ 117684 ▁nazir 117683 ▁coloc 117681 ▁11:00 117679 ▁filmer 117676 աձ 117675 ▁recomand 117675 ▁mắc 117671 ▁ארויס 117668 ▁Tó 117664 ጠቀ 117659 nisk 117655 ▁краі 117655 nemu 117654 ▁mesiac 117654 ▁इसे 117654 ▁머 117653 ▁maklumat 117651 ▁آدم 117650 ektur 117635 ▁Kerajaan 117635 ▁චිත් 117632 ▁enkelte 117628 ▁Amerikan 117627 ಾಯಿತು 117623 願 117618 ▁คลิป 117617 ရင္း 117616 ▁центр 117616 සර 117614 ▁результате 117614 քն 117610 ▁তা 117606 ምነት 117604 jähr 117597 ▁Magaz 117596 ▁aún 117596 ▁одговор 117595 ▁baga 117594 ukhulu 117591 ▁kurā 117590 ▁(11 117589 ধ্য 117588 ▁собира 117587 ▁први 117586 ▁arvio 117585 ▁frek 117581 ▁സൗ 117581 ▁trä 117577 ▁jogos 117573 ▁minä 117573 ▁августа 117572 ښې 117570 ▁Nim 117570 ▁περ 117569 ▁Bem 117568 ▁atrac 117568 ▁மாண 117568 ▁relação 117565 ▁ජා 117562 ▁strate 117555 mağa 117554 ▁ችግር 117553 ▁प्रत्येक 117552 偏 117552 ▁bật 117550 ▁diena 117550 ▁trè 117547 ▁Сред 117546 узе 117542 ▁Vladimir 117542 ▁tiasa 117541 ▁ज्या 117538 тердің 117537 츠 117536 ▁voksne 117533 แฟน 117530 ▁mujeres 117530 ▁wymi 117530 ▁Pren 117528 ▁materiál 117528 ười 117527 ▁बजे 117524 akhir 117522 ▁করেছে 117520 慢 117520 ▁válasz 117519 mö 117518 ▁პი 117518 ▁Catalonha 117517 ▁muaj 117514 ▁niem 117514 ▁тон 117512 эа 117507 ▁शुक्र 117505 ▁በሰ 117505 ubre 117502 ởi 117500 ▁هنوز 117493 ▁കേന്ദ്ര 117490 ▁mieste 117488 menti 117482 ぁ 117481 zimba 117480 ▁әкім 117477 ▁quảng 117474 áni 117471 ▁pent 117471 Dan 117469 ỏng 117464 ▁especi 117463 ▁journée 117461 ▁ści 117458 鲜 117457 ▁cauza 117455 ▁leti 117455 ▁Instituto 117453 ▁அடி 117449 Се 117448 ▁охо 117444 χές 117443 ▁নিয় 117439 komen 117438 ڑے 117438 ▁izvješ 117438 ▁وإن 117437 ▁cay 117436 ▁۱۲ 117429 ▁följ 117428 терна 117427 ▁isin 117427 จํา 117425 سون 117423 alc 117420 метри 117420 ▁naken 117414 ▁sistemas 117414 ▁અમા 117414 ▁öllum 117407 ▁компонен 117407 变化 117405 face 117404 ajak 117403 ▁፣ 117400 seb 117397 र्थ 117397 ▁õpp 117395 ▁Joc 117390 ▁sykje 117387 ▁əsasən 117387 ▁قائ 117382 ▁ಡಾ 117382 ▁βλέ 117378 ▁otáz 117377 ্যাল 117374 ▁gist 117373 izal 117371 ▁sortu 117371 ▁хэмжээ 117367 緊 117367 ▁Natural 117366 患 117366 ▁çalışmalar 117358 зви 117356 ▁manéhna 117354 ▁لع 117352 ▁rass 117351 gul 117347 ▁osvoj 117346 ▁Dzie 117342 ▁fotogra 117340 ▁ụmụ 117340 боль 117339 ▁ተከ 117339 ▁Micro 117338 一人 117334 стить 117332 ▁іншых 117331 ▁સૌથી 117330 ▁אג 117329 ▁असते 117329 ▁oke 117327 ciem 117322 ▁ប្រជា 117314 ▁(40 117311 ementer 117310 udde 117308 ▁oikein 117306 ▁Musek 117305 ▁والن 117305 ოვნ 117304 ▁MUS 117302 ▁йыйы 117302 ▁концеп 117297 ▁олардың 117293 ▁xén 117289 ▁xảy 117289 մամբ 117285 ▁nemá 117285 ▁Tru 117280 ເງິນ 117277 ندگی 117275 ▁dades 117271 फी 117268 ▁naib 117265 ▁gesinn 117262 :57 117253 annut 117251 ▁रू 117246 ustas 117244 ▁বন্ধ 117237 ▁πή 117236 ▁туған 117231 จากการ 117226 tifs 117219 ▁ула 117218 ▁tehnolog 117215 ▁nghề 117208 ▁ሁለት 117206 ▁वीडियो 117204 ▁возмож 117202 ודות 117198 ▁сар 117198 ▁êtes 117196 вов 117192 ▁ОТ 117188 긴 117186 inkel 117185 IME 117182 adam 117182 ▁lien 117181 เริ่ม 117177 ▁מוז 117177 ▁മറ്റ 117176 ▁заха 117175 ▁klasi 117173 IDE 117171 ▁оқ 117170 ▁sonuç 117165 unday 117164 ▁tawa 117160 ▁извор 117158 產品 117156 ▁دارم 117152 保险 117151 ▁işlər 117149 ▁Muz 117148 ▁Huawei 117143 ▁nagyobb 117140 ▁télé 117130 ▁ສໍາ 117129 ▁bölüm 117128 ▁okw 117128 ორის 117125 ▁tuz 117120 员工 117120 ▁Informatioun 117118 istian 117117 ▁breyt 117115 ▁РС 117114 ▁Living 117113 ▁אס 117113 ▁آورد 117113 ▁ຍັງ 117111 uyu 117110 ▁पर्य 117108 ▁հաստատ 117105 もある 117103 ▁radu 117102 ▁kiir 117101 بن 117100 ▁TM 117100 ▁جنوبی 117099 ▁കരു 117099 ▁fron 117095 inot 117091 კვე 117091 ▁ekz 117091 malıdır 117090 ▁Altern 117087 ▁ផ្លូវ 117086 ▁yog 117080 ▁በወ 117080 ▁일반 117079 ▁שאי 117071 ▁Respons 117066 ▁tratta 117063 ▁приема 117063 ivia 117054 usst 117050 ▁informace 117048 ▁Muuse 117047 ▁பண 117042 ▁ครั้ง 117041 ochobre 117040 athini 117035 жим 117035 ivä 117032 ▁στιγμή 117028 usio 117027 ಾನೆ 117024 ▁Pasa 117024 ▁blivit 117023 ▁гле 117023 ällen 117016 แข 117014 ▁powsta 117013 ņiem 117012 舉 117006 ▁Vak 117001 ▁ຊີ 117001 ▁satria 116999 ectura 116998 រង 116997 ▁bericht 116997 ▁agraman 116995 ▁олго 116994 ▁اڪ 116993 ▁Audio 116990 ▁mets 116986 ▁રૂ 116986 ▁νέο 116984 ▁Ela 116983 ▁eesmär 116983 μων 116982 ▁ואת 116982 ▁lon 116977 lež 116976 ▁ମୁଖ୍ୟମନ୍ତ୍ରୀ 116974 ▁escontra 116973 ரே 116970 ებისთვის 116965 ▁crist 116965 ▁garantir 116964 рото 116960 وقع 116960 ▁Geschichte 116956 ▁আপনি 116956 ▁dış 116955 ▁Disse 116952 ▁Torn 116952 ▁Gay 116951 ▁простран 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116569 ▁ڪندڙ 116569 ▁делу 116566 chei 116564 ▁Polo 116562 09 116560 ▁leeyahay 116559 оро 116557 ள்ளார் 116553 altro 116552 ▁Keh 116551 zew 116548 ▁declarat 116546 ▁ଲକ୍ଷ 116546 ▁adına 116544 ▁приме 116544 ▁müraciət 116536 :56 116535 áře 116529 ▁სან 116526 ▁forskning 116524 ▁сај 116524 ▁antal 116523 ▁заседа 116522 uus 116520 ▁kulo 116520 ம்பு 116514 ▁Raum 116514 ▁حوالي 116510 ▁είχαν 116508 ▁інформації 116506 olia 116504 өж 116502 ▁이야기 116501 део 116499 관리 116498 출장안마 116498 ▁сәйкес 116492 ▁جيڪا 116491 ▁Ente 116486 भि 116485 しよう 116484 ▁compt 116470 ợi 116466 ▁megfelelő 116465 殺 116465 ▁Tub 116463 ▁julka 116460 ▁menyatakan 116460 ▁Vz 116459 đu 116458 ▁evol 116458 ▁президен 116458 ▁мың 116455 ▁şəkildə 116452 ▁XU 116449 ▁tags 116437 帝 116436 ▁કર્યો 116435 ưng 116427 kohta 116424 ģijas 116423 օ 116423 Read 116422 ▁naut 116422 entrum 116420 แจ้ง 116419 ▁kleur 116418 ▁එකට 116414 ▁encu 116412 ▁амери 116410 ▁natt 116408 ▁pretože 116406 ▁ψη 116401 ▁hirup 116400 姆 116397 ▁leef 116396 ▁všech 116396 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▁ድረስ 116198 ייב 116196 ▁Result 116193 ▁(20) 116188 FM 116185 antys 116184 λλή 116181 akake 116177 iyoruz 116175 ▁پت 116175 されます 116175 ishini 116174 ▁करत 116173 kový 116169 ksta 116169 ório 116166 jia 116163 ▁Xbox 116160 ▁peux 116159 वती 116158 ▁rhyw 116158 زې 116155 ▁ਕਰਕੇ 116155 ▁üret 116148 ▁illustr 116145 ▁овде 116145 ▁teoria 116141 ▁einde 116139 ▁coraz 116138 ánica 116137 ▁faoin 116132 ціа 116128 ▁qis 116127 ▁ανε 116126 ▁korist 116125 ▁mohli 116123 ▁actor 116113 ▁vooral 116111 ▁År 116111 ▁ومع 116109 ▁Gali 116107 ▁shart 116106 ▁організації 116105 ▁ترا 116105 นคร 116102 ▁thử 116100 ▁kvinne 116099 ▁njeri 116099 ならない 116097 伯 116097 erau 116092 ніза 116092 दर्श 116088 ▁Young 116087 ▁invent 116087 爾 116086 ▁තිබු 116085 alizado 116083 ▁қамтамасыз 116082 ▁Tran 116079 ▁Pháp 116077 мес 116073 ▁ند 116072 ▁center 116071 ▁komple 116070 ërore 116067 ▁tens 116064 ▁pruž 116061 ▁Bid 116060 ▁ওয় 116056 ▁ಚಿ 116054 ▁СР 116051 APA 116046 મેન્ટ 116044 ▁पुरा 116035 ▁Mehr 116032 ▁solicit 116030 ▁추천 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115296 ▁Pern 115296 èrè 115295 ▁ďalšie 115293 ụọ 115292 ▁informacij 115292 tina 115288 lela 115283 ▁yazı 115281 ▁почу 115279 ຜ່ານ 115278 ▁වග 115278 ત્રો 115273 nenie 115269 ▁Pří 115266 ▁Тра 115265 ▁roka 115263 ▁seinem 115262 ▁городе 115256 ▁Sloven 115254 ▁kó 115253 ▁tygod 115251 ▁thiên 115248 ിയോ 115247 dere 115243 ▁ತು 115243 。「 115240 重点 115240 ▁kính 115239 Թ 115238 ▁Rok 115238 ▁Sebagai 115236 ▁quin 115233 ▁sari 115232 ▁яһа 115232 ▁Sicasí 115225 ▁Cag 115222 ▁तू 115222 딩 115221 ▁riig 115217 ▁elegant 115216 ▁mengikut 115213 ▁provo 115201 ▁പ്രവര് 115201 urang 115200 edera 115198 ▁prven 115198 λιά 115196 зата 115196 ▁pere 115195 саа 115194 ▁verlo 115194 ▁اندازه 115194 prü 115193 ▁Bush 115193 ▁juos 115190 ош 115189 ▁Kale 115187 ▁podej 115185 ▁känner 115182 ngal 115179 tret 115179 ▁диви 115174 ▁Fak 115171 esine 115170 МІ 115168 ▁Dí 115167 ▁العربي 115162 ▁الإن 115161 ▁πολ 115158 πή 115156 ▁mannurat 115154 ▁përfund 115144 ڻا 115143 ▁отырып 115142 ▁ቢሆን 115141 aysa 115138 сните 115136 ▁camere 115136 ିତା 115135 ▁ଘଟଣା 115130 োর্ট 115129 ▁Week 115124 ▁gampang 115120 iong 115119 ▁svijeta 115115 ▁következ 115113 əsindən 115112 ▁çësht 115112 llan 115111 滑 115111 ▁kamo 115110 egia 115108 βο 115108 ▁купу 115108 yanın 115107 ▁Rahmen 115107 ▁celebr 115107 ▁คํา 115105 වලට 115104 ▁Cath 115099 gör 115095 זש 115095 ▁poly 115094 nő 115093 ▁2005, 115093 ▁ydych 115090 ▁дам 115090 ¬ 115086 ▁oran 115085 ասան 115083 ▁всей 115083 ẫu 115081 ▁hamp 115078 ▁விடு 115077 itele 115071 ▁सेना 115068 ացված 115067 ▁asli 115067 ▁своя 115067 ▁управление 115066 ▁ifade 115065 kere 115057 фан 115057 ▁üld 115053 ▁pomoć 115050 ▁ndu 115049 ▁ակ 115046 ີມ 115045 ▁primeros 115041 ▁araç 115040 ▁gallu 115040 ▁जैसे 115040 賽 115040 ▁ہمیں 115038 ▁отново 115037 ▁프로 115031 ▁Интер 115024 ▁zodat 115021 ▁cron 115015 ▁משה 115015 ▁720 115014 ▁LV 115010 ▁ਮੈ 115005 ▁spôsob 115002 ▁daban 115001 ▁Dad 115000 嗎 114998 ▁Фі 114997 ▁Rights 114994 ርድ 114990 teja 114989 ▁atrás 114984 职业 114984 ▁узна 114982 ਲੋ 114981 ▁diqqət 114981 ▁1969 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114818 ▁mangrupa 114816 ▁pokrač 114815 okom 114814 ▁отме 114814 ▁यदि 114814 ዓት 114808 ▁κινη 114804 ▁һайлау 114799 esias 114798 ▁laporan 114794 ▁ყო 114794 ▁Президент 114793 ▁갈 114792 ▁spesiale 114791 ंमध्ये 114789 ▁kuji 114788 ▁UEFA 114787 ▁फै 114787 ▁село 114784 ▁delav 114782 ▁mera 114779 ▁Sf 114778 юд 114777 ▁eben 114777 ▁eventos 114776 ሠራ 114774 ▁tenim 114774 ▁२५ 114774 ▁Suomi 114773 ▁miembros 114769 ▁sehemu 114768 ▁ćemo 114768 ▁Pulaar 114766 ▁قومي 114766 经过 114758 ▁2500 114757 ▁remat 114757 ▁contrac 114753 ▁مرح 114753 ▁үнэ 114751 ▁Flu 114749 ゲーム 114748 ▁minsken 114742 ▁plec 114741 ▁그러 114741 ▁præ 114736 ▁Resp 114733 ▁ಬೇ 114729 ர்த்த 114728 เทศ 114726 ▁suara 114726 ▁podstaw 114725 жные 114722 ▁यही 114720 ▁آیت 114718 Facebook 114717 annte 114717 ▁Закон 114717 ▁chle 114716 ▁Kasi 114715 identes 114713 Ủ 114713 льныя 114712 ▁χρόνο 114711 ▁starten 114708 ▁Mô 114707 ekre 114706 ▁enem 114705 ▁იყოს 114705 ▁этому 114704 ▁ինքն 114701 整个 114701 ▁станов 114699 ▁اسپ 114697 ▁telefonu 114696 ▁certa 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▁hain 114555 irala 114553 ্যাপ 114553 јом 114550 ▁ABS 114548 iseach 114547 ိုင် 114543 ▁القانون 114542 ▁aik 114540 ▁ಮತ್ತ 114540 пара 114538 ▁finance 114536 眠 114536 व्या 114535 ▁Regierung 114534 ▁Vlad 114532 ▁التص 114531 ễ 114530 :53 114529 ளின் 114529 ▁سند 114524 ပညာ 114523 ▁berjaya 114520 ▁וויל 114520 huar 114519 ▁ekstr 114517 ▁SEK 114513 ਾਮ 114511 ▁časa 114510 rix 114509 ▁الشخص 114506 ▁찾아 114506 ussi 114505 推进 114502 ገድ 114499 銀行 114498 ▁määrä 114497 qubo 114495 ístico 114495 ▁Zdrav 114493 ▁تول 114493 ▁account 114491 ▁پنهنجو 114490 rija 114480 ▁ორგანიზ 114479 ▁រំល 114467 usty 114466 ▁stellt 114461 ▁Serbia 114459 ▁Ross 114455 ▁jasno 114454 ▁(24 114453 ▁ائين 114453 ▁attīst 114450 ▁primit 114450 ▁חוק 114449 ▁۱۳۹ 114449 ▁støtte 114445 ▁φύ 114444 ▁لغ 114442 ាការ 114438 ▁القر 114438 ▁Wizara 114431 禁 114431 csak 114430 ▁mosusu 114427 ▁πάρ 114427 ▁раза 114427 ▁ત્યાં 114425 ώνει 114424 ▁telefo 114420 ▁presyo 114417 ▁évi 114416 ▁จังหวัด 114416 ▁lepo 114411 emet 114409 ▁skrev 114406 ▁የሕ 114405 公開 114402 ▁Тэг 114398 ATAN 114392 OMA 114389 ▁takip 114387 ▁даде 114384 уына 114377 гран 114375 ▁মুখ 114373 ▁Bend 114372 धान 114371 ▁daun 114370 ▁szükség 114363 ▁ගෙදර 114363 ▁inför 114361 cją 114356 ፍት 114354 ▁چیف 114352 ಗಳಿಂದ 114350 ವಾರ 114344 ▁17:00 114343 ▁horiz 114340 ▁دہ 114339 ECH 114337 ▁Liter 114337 帮 114336 ђу 114333 ▁ರೂ 114333 niec 114326 ▁зво 114324 ▁різних 114324 ▁הור 114321 ▁kialak 114320 ▁iskol 114316 보험 114316 свя 114315 τηκε 114314 ▁ნაკ 114308 ▁നിറ 114307 ▁sjell 114305 ▁strokov 114303 ▁njohur 114301 ▁maail 114300 ▁दिल 114300 อนุ 114295 ▁fannen 114293 ▁sibh 114293 ▁časti 114293 ▁Pari 114292 顧 114292 вора 114291 වෙන 114291 ▁entsche 114291 ▁lwe 114288 ▁korrup 114285 ▁vinna 114283 atlar 114281 éré 114281 ▁говорить 114281 ▁wprowad 114278 asih 114274 ▁يوه 114272 ▁đảo 114271 게임 114270 ▁якого 114266 ▁Summer 114264 ▁वन 114259 that 114256 ▁humano 114256 amant 114249 נוך 114247 лног 114245 ▁kaart 114245 ▁118 114242 ▁tweede 114242 ເຮັດ 114240 ▁categoría 114236 ▁ինձ 114236 edt 114231 ▁iedereen 114231 1). 114229 rgan 114228 ▁பார்க்க 114228 ▁buz 114223 ▁sisse 114223 iii 114218 ▁tratar 114217 ▁presencia 114216 幫 114214 ▁манасти 114213 ▁Valsts 114207 ▁BAN 114203 ▁Find 114202 ▁KAN 114200 误 114194 ▁مطال 114192 ▁પ્રા 114190 ▁მეტი 114190 ▁szyb 114188 ▁Kategori 114187 ▁ň 114186 ▁そして 114185 Esc 114183 ত্যা 114181 ▁naših 114178 ▁آموزشی 114175 ▁Lanka 114169 ▁ជំន 114169 ▁خام 114166 ▁مى 114163 vět 114160 znych 114157 ▁արձ 114154 ▁ਕੈ 114153 isela 114152 ▁пайдалану 114152 рения 114151 ▁donner 114150 ▁musul 114149 athu 114147 ▁wp 114142 ▁Soo 114134 ▁etik 114134 ▁conx 114133 ▁מאל 114132 ▁الطبي 114132 ▁रहने 114132 ▁احتجاج 114131 ▁vezet 114130 ▁ensimmä 114129 Ho 114127 urais 114127 უძ 114127 ▁olup 114126 ▁внутрен 114126 ційних 114124 ▁drob 114124 现代 114120 risida 114118 ▁አካባቢ 114118 amanan 114115 ▁проведения 114115 grond 114112 ▁келе 114112 対 114109 мән 114108 ▁februari 114108 ▁Merkel 114103 ▁адпа 114101 ക്കാര 114098 ▁rieš 114092 ▁spesif 114090 ▁atribu 114086 ▁UD 114085 σσ 114084 ▁වශයෙන් 114084 ▁விள 114083 ▁bolesti 114082 ▁मीडिया 114082 timos 114081 adlo 114080 kész 114080 ▁Analytics 114079 ያው 114078 jik 114076 ▁दाख 114076 Saya 114074 ▁geçen 114071 ▁адво 114069 سے 114068 ▁spis 114068 ▁ģ 114062 ▁वार् 114060 ונגען 114059 ▁яву 114059 ▁tabl 114056 ▁대상 114055 ▁génér 114053 ▁علاوه 114052 たくさん 114052 ▁aveva 114051 дн 114047 ▁Gru 114047 ▁העיר 114046 ▁алба 114039 ▁Johan 114035 ▁proiz 114026 ▁dirección 114025 ▁hvem 114024 ▁razgov 114023 ▁gén 114013 ▁teď 114011 ▁Fut 114009 ▁сиг 114005 検 114005 ırlar 114001 ▁werde 114001 λες 114000 ▁ofrece 114000 ▁asocia 113994 ▁közül 113993 ▁thái 113990 ▁halaman 113987 കും 113985 ▁Alan 113982 ▁habita 113980 хол 113977 ▁Markt 113976 ▁pubblico 113976 ▁ڪه 113976 aug 113975 ▁elfog 113973 ilise 113971 υχ 113969 ▁entrevista 113969 ▁१८ 113969 ▁آثار 113968 ▁percep 113967 ונט 113966 ▁ນັກ 113965 miştir 113964 umam 113964 און 113959 ▁koszt 113956 കോ 113952 ▁carrer 113951 ierta 113950 ▁kopā 113947 qli 113946 uszcz 113944 ▁ვინ 113940 ▁280 113939 rina 113938 idhne 113937 ▁забезпечення 113936 енсә 113933 ▁γυνα 113933 ▁delic 113932 ំហ 113931 ▁chỉnh 113931 чком 113926 म्म 113924 ▁vivir 113921 ▁vstup 113921 จ่าย 113919 рының 113912 ത്തുക 113912 ▁usein 113911 гат 113910 ੍ 113910 ▁० 113910 ▁ਲੱ 113910 ▁VM 113907 ▁مشهور 113904 ▁складу 113903 ▁heures 113898 ▁mety 113897 关键 113895 ▁mů 113894 тија 113892 ístiques 113891 prise 113887 ▁виконання 113887 ческим 113873 ▁Rasulullah 113872 ▁duket 113872 šni 113864 ▁pages 113861 ▁нищо 113860 ▁līdzek 113858 order 113850 ▁inan 113850 ienen 113849 ▁خبرې 113841 ▁amiga 113835 posición 113834 yaka 113834 ಕಿ 113833 ▁במקום 113832 ▁þur 113831 バイ 113828 ▁Север 113826 դր 113821 ▁읽 113818 ovice 113817 ▁የሚያስ 113817 ▁zahrani 113814 ▁тото 113812 돼 113812 ख्या 113811 ▁طاقت 113811 ▁inson 113809 рыла 113807 خی 113803 ▁дву 113800 ▁læg 113799 ▁Ida 113798 árd 113796 ▁ხალ 113795 ▁Lao 113791 ▁różnych 113790 ▁Архи 113789 ▁bade 113787 ▁trabayu 113787 गु 113784 ▁დარ 113783 ▁არს 113779 ▁númberu 113776 ologiya 113775 فون 113775 haidh 113774 ที่เป็น 113774 ▁Estr 113772 ▁façon 113772 ▁บาง 113772 ිකා 113769 ëss 113768 ▁disfrutar 113766 Nkosi 113764 ▁ਸਿਰ 113760 ფიქ 113759 ▁további 113759 、1 113755 ▁dám 113753 aneho 113751 ▁tecnolog 113749 ▁omuhle 113745 ▁unders 113745 ▁ਕੁਝ 113745 aksid 113744 ▁ziua 113742 ▁운 113741 ▁staff 113739 erak 113738 ▁שיי 113738 anzi 113735 ▁eval 113728 ▁lã 113727 খন 113725 ▁angin 113725 ▁mwan 113723 ▁पक्राउ 113723 ▁धन 113722 ▁girls 113714 โค 113713 ▁ಮಾತನಾ 113709 ▁Zug 113703 ▁rə 113703 ▁Zato 113701 ▁ਉੱ 113701 nyv 113700 ▁behe 113700 ▁bëhet 113699 ▁Fakt 113698 chs 113695 ▁besz 113692 ▁başa 113691 ▁Story 113686 гдаж 113684 ▁skapa 113683 ▁لهم 113679 ▁abrir 113673 ▁במח 113673 ▁ଡି 113667 ចំណ 113666 ▁hatten 113663 fna 113658 ▁بهداشت 113655 اهم 113654 ▁nakap 113654 ▁الاقتص 113646 ▁diagnos 113644 ▁البل 113643 ▁ngi 113642 ▁مسئ 113638 สรร 113635 бет 113632 កាស 113631 ▁Tetapi 113629 σαμε 113624 ▁ဓ 113623 unst 113622 ▁THE 113622 ysan 113621 ▁Төр 113620 ▁اض 113620 σματα 113616 rate 113615 ષ્ 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▁allerdings 113422 ▁المخ 113422 ickej 113420 imina 113419 वरी 113415 ેશ 113411 ▁způsob 113411 ▁Спорт 113407 intah 113404 ▁szép 113396 ▁тере 113396 ▁ႏွစ္ 113391 ▁пону 113390 વાનો 113379 :49 113375 чень 113372 ▁Petra 113371 ▁Vie 113368 ideak 113365 ларында 113364 ▁ກໍາ 113358 ▁түү 113357 নী 113352 ▁Ysgol 113350 ▁dianggap 113350 ▁نزدیک 113350 sama 113348 છ 113348 ▁syg 113346 팀 113346 ▁സംസ്ഥാന 113345 ▁veilig 113338 ▁peserta 113337 ▁erabil 113336 šek 113334 ▁znan 113334 ▁πλέον 113331 ▁förb 113330 alulekile 113329 ikiwa 113327 ▁lĩnh 113327 ยว 113326 ระยะ 113324 fiya 113320 станови 113314 ▁espir 113314 ▁faktiskt 113311 ▁əraz 113308 ▁Һеҙ 113308 ▁måte 113307 ▁чар 113307 ▁Kats 113304 ▁demais 113300 ▁Staats 113298 ▁recuperar 113298 ▁ସେହି 113294 :54 113291 žin 113290 ▁demás 113284 ▁مول 113284 ЧЕ 113282 ▁محافظ 113278 ▁ಸಾಧ್ಯ 113277 ▁ligi 113273 ▁изменения 113270 押 113270 ▁amet 113268 ▁асуудал 113267 ▁вози 113264 ▁Digit 113256 ▁ozn 113256 ▁друге 113254 ენტი 113253 ▁Cook 113250 ▁hita 113248 सल 113239 ▁пък 113238 ▁সংস 113236 ▁مرة 113234 सिक 113233 ▁aiki 113231 ▁административ 113228 运动 113225 ▁hekt 113224 ڑی 113223 ▁અમ 113222 ▁knj 113216 ▁месяц 113216 šina 113214 ▁oldukça 113213 ▁ireny 113209 ▁ಜಿಲ್ಲ 113209 ▁179 113207 ▁נוספים 113206 landet 113203 ▁beau 113199 iongozi 113197 vida 113197 ▁malik 113197 ▁huyện 113196 ▁процесс 113193 ▁kjærlighet 113189 ▁mapan 113189 ▁Happy 113184 keta 113182 ▁ҡарата 113182 ▁دختر 113177 ▁saben 113174 ▁көрсеті 113173 ▁HAR 113171 णि 113168 ▁Govern 113167 ▁pregled 113167 ▁σω 113166 ▁asos 113164 ▁ителә 113164 ▁Cost 113157 ▁Vär 113156 ینگ 113152 ▁karto 113151 র্ম 113148 ▁berarti 113144 сування 113139 ▁nadumaduma 113135 ▁मन्त्रालय 113135 ্তা 113134 ård 113131 ▁وزن 113131 ▁Legg 113129 يده 113126 ▁%. 113123 ûs 113122 ▁осе 113122 ėjų 113119 ▁Marco 113116 ▁ਪਿੰਡ 113115 ▁sicurezza 113112 ▁कल 113112 ██ 113110 øren 113108 ▁choses 113108 ▁Коли 113105 ▁रुप 113102 ▁Bán 113101 ▁ವಿರುದ್ಧ 113101 ▁եղ 113098 šanje 113097 ▁gebeur 113094 ▁xiri 113086 ▁jū 113085 錢 113085 ▁01.0 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112920 ▁გამოი 112920 ▁участи 112918 ▁kohal 112916 様々な 112914 ▁Báo 112911 lova 112909 ▁dobře 112904 辆 112904 ക്ഷി 112898 ▁duha 112896 促进 112896 คอน 112895 ▁қаржы 112888 篇 112887 ▁දේවල් 112884 ▁схе 112883 ▁माओवादी 112881 ▁interac 112878 ▁maksimal 112877 ▁cilët 112872 Dr 112871 不足 112871 ನ್ನೂ 112867 ▁илүү 112864 ▁φυ 112861 ଠାରୁ 112860 ▁பழ 112857 ▁хүртэл 112856 ▁болуы 112853 ದ್ದು 112852 ▁хәҙер 112844 mæ 112842 inei 112839 बहादुर 112839 ▁գործող 112837 cada 112835 ▁próxima 112835 river 112834 ▁frit 112834 ξαν 112833 ίζεται 112828 prot 112825 ຕົ້ນ 112825 hig 112823 ▁drum 112819 edades 112818 ▁Asociación 112815 ▁технологии 112814 hung 112813 ▁homes 112813 ▁면 112809 žili 112807 ▁Wier 112806 ▁cie 112800 ▁لش 112798 แหล 112797 Up 112794 終わ 112792 ▁žm 112791 ոնի 112789 ▁Hack 112788 ▁mulle 112788 ishe 112785 ွဲ 112784 續 112784 ▁clienti 112782 ▁destina 112782 rekt 112781 ोरी 112781 ▁precio 112780 ▁aleg 112776 ▁ПРЕ 112776 ▁предприятие 112775 ▁kéo 112771 ▁2000, 112770 ▁nòrd 112767 ▁numa 112765 ▁ubang 112764 ▁അദ്ദേഹം 112762 ▁구성 112761 ▁анх 112760 ቶችን 112757 ▁tận 112754 ▁блог 112753 ▁ويندي 112753 ▁tiksl 112750 म्स 112749 បុ 112748 ▁FN 112748 ▁genç 112747 ▁humb 112746 ▁tám 112746 ▁стоимость 112744 νομ 112736 ▁വള 112735 ▁សារ 112734 ໄດ້ຮັບ 112733 ▁γνώ 112732 ▁concept 112728 ▁имуще 112728 ▁jäsen 112724 ▁января 112724 ▁razón 112721 ▁آخ 112718 发展的 112718 лектәр 112717 ▁Hver 112715 ▁Badan 112708 ▁далеко 112708 madan 112707 პო 112702 ▁ကျ 112700 ▁공개 112700 tabil 112691 ▁155 112689 ടേ 112685 ▁ସହିତ 112684 ▁Pard 112683 ▁?????? 112673 ▁potřeb 112671 ▁fjal 112670 ให 112668 สํา 112667 ▁eziy 112664 ▁hini 112659 ▁vzd 112658 jegy 112657 ណី 112655 ▁2561 112654 ▁ירושלים 112653 ▁drop 112651 ុខ 112648 ▁индустри 112643 ▁кіраўні 112642 داث 112635 驚 112635 жар 112632 ▁imun 112631 ▁ילדים 112631 ▁Phetolela 112626 ▁Ljouwert 112625 खर 112624 inje 112613 ндер 112612 ▁kaug 112609 ιον 112606 ▁дија 112605 ▁MD 112604 ▁дале 112597 ▁rych 112589 ▁Jordan 112585 ▁prijs 112584 ▁moe 112583 пры 112582 ்கிற 112581 ▁excelente 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111623 ▁kilometer 111622 varer 111620 ▁வரும் 111616 ihkan 111614 után 111614 ▁faiz 111611 astal 111607 ▁internas 111606 ▁berjochten 111605 ▁однако 111605 duğu 111604 ▁FI 111604 하면서 111604 ▁Staat 111600 ▁είδ 111597 ▁जोड 111597 ▁22- 111596 ▁calitate 111588 みに 111585 ▁Lachin 111581 ▁शक्ति 111580 ▁įvairi 111577 杨 111577 iriza 111576 ▁सामने 111573 itatud 111571 iyasi 111571 ▁йәшәгән 111570 nep 111569 ขน 111566 สุขภาพ 111562 ▁bede 111559 ▁nemoc 111558 ственный 111556 ▁үткән 111554 者が 111550 ▁ukaz 111549 ▁самого 111549 ▁मध 111547 သတင္း 111543 ▁dohod 111541 ▁абсолютно 111539 ▁азамат 111539 ▁ჯერ 111536 ▁Fyw 111533 ▁ordu 111531 ಂದರೆ 111530 ▁அழ 111528 最终 111526 ▁сравне 111523 ▁αξιο 111520 ▁diken 111519 汇 111517 ▁kaasa 111515 ▁naturale 111515 صح 111508 ▁particuli 111508 ▁לז 111507 ▁hing 111504 کرد 111502 ▁Hez 111499 ▁MediaWiki 111498 ђен 111497 ▁napis 111495 ▁ຂ່າວວີໂອເອລາວ 111494 시아 111493 imov 111492 ................ 111488 ▁fredag 111486 ▁سول 111485 ▁electrònic 111484 支援 111484 했습니다 111483 сами 111480 ▁gelede 111479 ▁предоставля 111478 ▁dvě 111476 ▁бидејќи 111476 ▁දවස 111476 ▁carb 111472 φαν 111470 ▁takim 111470 ▁проис 111470 ▁فشار 111468 ▁Жо 111467 ಕ್ಕೂ 111463 okuba 111460 ▁Jal 111458 ▁gawin 111457 ▁판매 111457 ▁ҡалаһы 111455 ▁kısa 111454 дят 111451 ខាង 111451 ▁týd 111451 ▁йор 111451 ▁әммә 111451 ▁ວີໂອເອລາວ 111451 ಹಿ 111448 ▁rà 111448 ▁مشار 111443 ▁Kéier 111434 ▁nisy 111433 ▁xid 111433 ▁අපිට 111428 を出 111425 ▁miš 111418 ક્ત 111417 ▁hujjat 111417 UNT 111411 فرنس 111410 ▁cateva 111407 ▁طبق 111403 刑 111401 Ї 111399 ▁kiz 111398 ▁gebied 111397 ▁halen 111393 ▁стол 111393 ▁විම 111393 ▁Batu 111389 ▁интерв 111389 ▁หม 111389 éma 111387 ▁कोण 111381 ▁atbal 111380 ▁piè 111379 ▁پنهن 111375 området 111372 ▁תמ 111370 ▁уна 111368 ▁דין 111368 лира 111366 കളും 111366 ▁měla 111366 ▁sodi 111365 ▁Sever 111356 ▁masal 111356 ▁seront 111355 ▁keess 111350 ▁ਸਕਦਾ 111350 ▁léir 111346 ålet 111345 γής 111341 bee 111340 디어 111340 ▁नेपालमा 111339 ▁serait 111338 ▁imagin 111337 veu 111336 ▁ответ 111333 pea 111332 tavat 111329 ▁eski 111328 ▁Akhe 111312 ▁kore 111310 ▁kapcsolat 111309 ajja 111307 ędzie 111305 My 111296 စၥ 111296 ▁concurs 111289 ▁Кана 111288 ▁Maga 111285 ▁привле 111285 नात 111284 ▁ონლაინ 111274 ▁Göteborg 111272 ▁tvo 111272 ▁Kate 111269 riko 111268 ringer 111262 ▁العديد 111262 tsz 111260 ▁hätte 111260 ▁manžel 111259 ▁ଦୁ 111254 ▁Forex 111253 ▁elektrik 111248 ▁ಬ್ರ 111245 ▁melhores 111243 ▁درج 111239 ▁zároveň 111238 ˈ 111236 riba 111226 არჯ 111224 alamu 111217 ▁μή 111217 ▁kadakuada 111214 ▁suhte 111214 ▁ерөнхий 111214 ▁වලින් 111212 бами 111210 ▁הייתה 111207 ▁ઉપર 111205 ▁ystod 111203 ▁trochę 111201 阶段 111198 ▁Sự 111197 ڙهي 111196 ▁чувства 111195 liches 111193 ▁También 111193 ныг 111191 ▁noong 111190 任务 111190 ▁buc 111186 ▁Ngoba 111184 üntet 111182 ▁својим 111182 кового 111181 iero 111180 észet 111179 ▁ahịa 111179 ющая 111178 ▁וק 111178 ▁पाँच 111175 ▁Trần 111169 kó 111168 ▁Trib 111166 لح 111163 ▁αυτές 111162 uthenga 111158 ▁ምር 111158 ▁lartë 111154 ▁maintenant 111153 ▁δω 111153 ▁নেই 111153 ▁wars 111149 ▁krem 111146 اپ 111142 ▁Baịbụl 111142 ūst 111139 ▁ideale 111139 ▁malamu 111139 რივ 111133 ▁karin 111132 ▁neiz 111131 ▁ງ 111131 ▁विरोध 111127 ▁আগে 111125 лба 111124 ftar 111123 ▁kapal 111118 ▁більш 111118 ▁doctor 111113 szám 111112 ктар 111112 »، 111111 خب 111111 estru 111110 ▁Ofer 111110 ▁bakit 111110 的情况 111108 ▁107 111107 ▁ओर 111106 elez 111102 ▁korup 111101 一篇 111101 ▁Dk 111099 pies 111097 ▁Prot 111096 ▁Pik 111095 ▁Յ 111094 ▁میر 111089 ୋର୍ 111086 ම්ප 111078 ▁comand 111078 ▁lidí 111070 สัญ 111069 ätzen 111065 APP 111063 ವಾಗಿದೆ 111052 宜 111051 εργ 111049 ▁Centrum 111048 ▁ryg 111048 ป์ 111046 ▁comida 111044 ISH 111040 etiti 111040 бәт 111040 ▁happy 111039 ▁misk 111038 ต่อไป 111033 ▁fjalë 111029 ▁ည 111029 сіне 111027 ▁१६ 111027 родна 111026 ▁muus 111025 ▁formato 111022 trat 111018 вають 111017 ▁adresu 111016 ▁viloyat 111016 lina 111014 ▁جهاز 111013 zkod 111011 ▁डो 111011 ▁Psych 111008 ▁khas 111005 ohet 111002 нис 110998 ▁ଆଗ 110998 ▁Щ 110994 ▁Piala 110992 ▁прожи 110987 вное 110983 iteti 110982 ▁דאָ 110978 ▁प्रव 110978 кава 110976 ▁گی۔ 110976 ваюць 110972 ▁stră 110972 ▁посети 110968 ▁сті 110968 ຍັງ 110963 ▁Arsenal 110961 ▁Nguyên 110961 ▁batzu 110961 ▁escolar 110955 ▁mascul 110955 ▁mait 110954 ▁المق 110954 ▁перио 110951 ταση 110948 भव 110947 ▁depres 110947 eggi 110935 意识 110933 ▁MIL 110924 roda 110923 ανα 110923 aleen 110921 ▁sinni 110921 ▁محسوس 110921 ործ 110919 ავლ 110919 ▁emot 110918 ፊት 110914 ▁josta 110913 ▁Né 110912 ▁koncep 110909 kui 110905 ▁Украина 110905 ▁бусад 110905 ësit 110904 ▁stod 110904 ▁Ті 110903 ▁ከሚ 110902 ▁Cò 110899 udzi 110897 ▁изпра 110894 映 110889 ▁असं 110888 ບານ 110884 ▁registre 110882 ▁газе 110882 ▁أق 110882 ługi 110881 ▁elok 110881 ▁singur 110881 ପରି 110880 ▁vivo 110879 ▁protein 110878 мовы 110875 ▁alkaa 110875 ▁största 110872 opis 110868 ԱՏ 110868 ▁אצל 110862 ▁ottaa 110861 òrta 110858 ▁प्रदर्शन 110857 діо 110853 allet 110851 ▁udal 110851 ▁loom 110847 ▁ټاک 110847 ဲ႕ 110844 ாள் 110843 ገና 110843 ▁udah 110843 icom 110842 ▁szok 110838 होस 110834 hatikan 110833 ▁прокура 110832 ▁үҫтереү 110830 ▁буйы 110826 ايو 110825 ▁Visos 110821 ▁década 110820 ▁feste 110820 ▁최대 110820 mut 110818 ▁зві 110815 ▁წევ 110815 বন্ধ 110810 ▁kasla 110805 ▁캐 110804 ▁недвижи 110802 ▁نصب 110796 ສູງ 110791 ួ 110790 创业 110790 ڃ 110789 ▁diy 110787 pras 110786 ▁intellig 110783 ▁چیزی 110782 ર્થ 110776 ▁případě 110772 нына 110768 ▁Bey 110764 期间 110762 ▁leen 110759 Men 110755 ▁відповідно 110750 ▁ನಡೆದ 110750 ຈັດ 110747 лыҡтар 110746 ▁ഡ് 110744 ▁nggak 110743 ▁ရွ 110743 ▁поро 110741 τήσει 110740 ▁quil 110739 നായ 110737 ▁permis 110733 ▁redd 110731 FP 110730 やす 110730 ıkları 110729 ▁சினிமா 110728 ▁ପୁର 110727 amassa 110726 ມັນ 110726 aime 110725 fina 110725 ДС 110723 ัต 110723 ▁زي 110723 ▁dempuèi 110720 ▁додека 110720 ▁Fè 110719 וכה 110718 fallen 110717 ▁думаю 110717 ▁årets 110712 ▁рам 110712 гани 110703 ▁található 110703 ▁φό 110702 ▁Universidá 110701 ▁irin 110701 ▁требования 110700 τροφ 110699 രായ 110699 ▁สน 110699 utsche 110698 ಕಾರಿ 110698 ▁(18) 110690 ▁винаги 110687 ▁방문 110685 ഡിയ 110682 eph 110681 ▁ausg 110675 dè 110674 ത്വ 110674 กาย 110672 ▁ಆಟ 110671 ▁göst 110670 ▁รถ 110664 ▁старо 110660 ▁הוד 110660 rene 110657 ▁қандай 110654 urun 110651 ▁непосред 110651 ▁hvilke 110650 ▁stiu 110648 ▁руковод 110648 ▁Hed 110647 ▁Prid 110644 altres 110643 kauf 110640 ▁ಹಾ 110639 Tube 110636 ચી 110636 ▁Want 110635 lát 110632 ▁ähn 110631 ▁Tanpa 110628 ൂടെ 110627 ▁रुपैयाँ 110617 ▁serán 110608 รก 110604 ▁Maha 110602 imize 110601 อื่นๆ 110601 ▁Indien 110600 ຕ້ອງ 110597 ▁Cyngor 110595 ājas 110590 ▁ប្រធាន 110590 ▁altra 110587 ▁เส 110584 ▁management 110582 ▁formal 110579 ющее 110578 団 110575 κέ 110574 ▁pilih 110572 কর 110570 ▁साम 110569 ZT 110567 škin 110565 ▁حسين 110564 ▁hơi 110561 ▁seksual 110560 ▁оце 110560 זער 110559 ▁piscina 110557 ilih 110554 ញ្ 110554 因素 110552 vic 110551 ▁পারে 110551 一緒に 110551 ▁arz 110548 ▁kutumia 110544 ▁Quảng 110543 ரியா 110540 رخ 110538 ▁kasuta 110538 ▁يقول 110536 الد 110535 มพ 110532 ▁japon 110530 ▁ខ្ល 110530 ▁Bana 110523 となります 110523 isco 110517 ▁mái 110515 ▁שבת 110513 ▁ventil 110510 ▁Kësh 110509 ▁מול 110506 ▁Geg 110496 ▁जिल्हा 110496 ▁ית 110495 ▁מן 110494 横 110494 itzak 110493 ▁teag 110492 ▁राष्ट्रीय 110481 ච්ච 110475 ▁اجرا 110470 emény 110469 ▁جاتی 110468 ▁რადგან 110468 しかし 110467 σμ 110466 Kh 110464 ▁zakona 110460 ▁uyğun 110459 cine 110457 ▁rę 110457 ▁osoo 110455 ▁rasti 110455 YT 110454 ▁ምስ 110453 ▁gereken 110450 េល 110449 ર્ડ 110448 ▁βρίσκεται 110444 ▁производства 110438 ແກ 110436 ▁հավաք 110434 ▁몇 110430 ▁jaringan 110429 なくて 110427 ▁fark 110425 ▁Bucuresti 110422 ▁borde 110422 ▁oamenii 110417 ▁Hala 110411 क्षक 110410 ▁вижда 110408 тный 110405 伸 110404 ▁újra 110402 ▁Šis 110399 ▁көнө 110399 케 110397 께 110392 ▁hannde 110390 ▁sebagian 110389 ▁talen 110389 ▁sheria 110388 sí 110385 ▁Рэ 110382 ▁Wien 110381 これは 110380 ▁pierws 110379 ▁fó 110378 ▁Дар 110376 ▁joan 110375 ▁происходит 110372 ▁۲۰ 110372 ”; 110367 ▁تصمیم 110366 ▁защита 110365 ▁szükséges 110364 ▁enig 110359 òp 110353 ▁Իր 110350 ▁इं 110349 ▁ykn 110348 ▁допомогою 110347 xil 110345 ေသး 110345 шува 110344 ▁yag 110337 主席 110337 ▁Ω 110331 ekez 110330 ▁крај 110328 цето 110327 ▁کیا۔ 110327 ▁ቴ 110326 ▁dash 110325 映画 110325 ುತ್ತಾರೆ 110321 âte 110320 ▁ਮੌਤ 110319 ▁റോ 110317 ▁Aarbecht 110313 ▁senang 110313 ▁aginggana 110312 ▁revel 110311 тенә 110310 ▁ընդուն 110309 ▁introdu 110307 ớ 110306 ▁وسي 110306 ▁Auk 110305 ▁steet 110304 构 110302 jawab 110300 tiku 110300 ▁ڏانهن 110300 ▁cậu 110299 ▁ajuta 110296 ▁मार्ग 110296 niejsze 110295 ▁Mud 110291 ▁tjed 110282 ▁tjejer 110282 ▁selvfølgelig 110281 体育 110281 agawa 110279 ▁sebi 110279 ▁diel 110278 çat 110275 沉 110271 ▁ცი 110266 ▁handel 110265 ▁φωτογραφ 110264 ECT 110260 ▁сайту 110260 ▁государственной 110259 льник 110255 лег 110253 ▁pasirink 110245 ▁llo 110235 ▁doğr 110229 ▁GM 110228 ▁cek 110228 ▁Ameri 110227 áli 110226 ▁terkait 110224 ▁ultra 110224 妇 110222 ▁resten 110221 дук 110220 ▁Selbst 110219 ▁बजार 110213 ▁08. 110207 ▁Harr 110199 ▁Londres 110196 ▁нього 110196 ▁cock 110191 ▁vaqt 110190 ▁145 110187 ▁আহ 110187 ڈر 110186 ▁ទំន 110186 oyin 110184 øker 110179 ▁ogled 110179 ასწ 110177 ▁Երեւանի 110176 ambana 110175 ▁personen 110172 ▁èsser 110170 ▁діа 110170 နား 110168 ▁edell 110160 ▁зато 110159 ▁ചെയ്യുന്ന 110159 ▁bidez 110154 äst 110153 শন 110153 ▁хво 110153 ▁əməkdaş 110152 ▁웹 110152 ▁հասարակ 110150 ▁ਪੁਲਿਸ 110148 ▁départ 110145 ادم 110144 笔 110143 יינים 110141 !.. 110140 anon 110138 ovao 110136 ▁held 110136 ▁isik 110136 orsch 110134 ீர் 110134 ▁запре 110133 ▁kontinente 110128 zent 110123 irao 110121 ▁пру 110120 Om 110118 ▁1999. 110112 ▁Amen 110108 重大 110106 alaka 110099 тър 110098 ▁ਵੇ 110097 については 110096 imid 110094 ▁ծն 110093 گیر 110092 ▁ير 110090 ▁kır 110085 neho 110083 ひ 110083 ▁söyledi 110082 ojan 110081 ▁jooksul 110081 ดิ 110079 ▁бәй 110079 ▁defens 110074 οινων 110072 ▁Рэспублікі 110072 ▁įraš 110071 ▁왜 110069 ▁Flug 110068 ▁нэм 110065 pá 110064 յակ 110064 ▁Recep 110064 өз 110063 ▁ретінде 110057 井 110057 නත් 110056 ورس 110053 ▁spoz 110053 ▁Educación 110051 riend 110048 bergi 110045 ▁Gore 110044 ചി 110043 ▁Diel 110043 start 110040 ▁ruim 110032 issenschaft 110030 ▁cał 110030 ▁buổi 110027 ▁otú 110025 ▁tapaszt 110025 ▁sayı 110023 otov 110020 arov 110015 ▁воло 110012 ▁pripra 110010 ▁gila 110006 intaa 110004 ▁Erdoğan 110004 ▁آنلاین 110003 ▁ରହି 109985 ▁подкре 109974 ▁pelas 109970 λογή 109967 ▁mies 109967 ていない 109964 ▁պահանջ 109960 akes 109958 λημα 109958 ▁енгі 109955 ▁инфраструк 109953 ▁prís 109952 分钟 109952 ovno 109950 ▁glaz 109949 သံ 109948 збе 109946 ▁צע 109945 ▁قی 109945 ▁байгууллага 109944 форму 109943 ▁daiteke 109943 ▁feu 109942 ▁ડે 109940 ▁તમને 109939 ▁chin 109938 ▁सञ्चालन 109938 ดูแล 109935 стин 109931 ▁질 109931 ▁realtà 109929 ▁дзя 109920 ▁ಕಲ 109919 サー 109917 365 109916 ▁שק 109916 orsunuz 109914 κτη 109913 ▁anc 109913 ▁чак 109910 ▁хөрөнгө 109908 ▁каждого 109901 敏 109894 ▁pekerjaan 109893 ▁אוה 109891 ▁export 109888 kein 109887 ▁herr 109884 にとって 109884 ▁Wannan 109876 чава 109875 でお 109874 itelji 109871 ▁região 109871 ▁थापा 109871 ικη 109868 ท้อง 109862 ▁rád 109861 тады 109860 boek 109858 feito 109857 ▁imah 109855 ñes 109853 atter 109851 ▁stats 109849 маг 109848 ▁siyos 109847 ▁ян 109846 ▁потенци 109845 කම 109844 ▁možnosť 109844 ▁ଅଭିଯୋଗ 109843 ▁регистра 109839 odia 109836 ▁dituen 109836 ▁Chương 109835 ▁barangay 109835 ▁acqu 109827 ▁אינו 109827 naval 109823 ▁sni 109818 ▁ಪೋ 109815 漫 109815 ▁உரு 109813 esty 109811 ▁wek 109808 ຫວຽດນາມ 109802 年代 109802 ▁درمیان 109801 ▁विश्वास 109794 ▁Zes 109790 istor 109789 필 109789 ▁drugo 109784 ▁Aby 109782 ▁boca 109782 ▁dèidh 109782 ▁Margar 109780 ząc 109779 formen 109772 ▁taim 109772 鮮 109771 nå 109770 ▁विद्यार्थी 109769 ▁comez 109763 ▁nəticəsində 109762 .? 109756 ift 109754 ▁inglés 109754 ▁fëmij 109753 ▁କରିବେ 109753 ▁subjek 109750 ▁uchaguzi 109750 ▁laawol 109748 ▁வரை 109748 ደት 109745 ▁וס 109741 ▁আশ 109738 ▁овом 109737 чук 109734 ▁બની 109733 ▁Slovenija 109731 onés 109730 ▁Սարգս 109730 ▁فلسط 109727 imente 109724 ▁bölg 109723 ▁ድር 109723 ▁garder 109722 talet 109717 സ്ഥാന 109716 ▁stwor 109716 照片 109714 สะดวก 109713 ▁Мер 109712 ▁principios 109711 ▁المشار 109711 ▁össz 109709 ▁λόγω 109704 ▁Nig 109701 ▁huku 109701 障 109701 ▁Вре 109699 ▁مقدم 109699 രിക്കുന്നത് 109697 ▁nagara 109692 ▁රදේශ 109692 ahon 109691 hairt 109691 ▁Biblioteca 109689 логиялық 109688 ▁Avy 109685 災 109679 ▁نمونه 109677 רע 109672 סטע 109669 ▁DIS 109669 ▁defnyddio 109669 ▁Total 109667 ▁mês 109667 狗 109660 ▁එසේ 109659 parang 109658 ▁ભાવ 109658 மன்ற 109654 ▁Πριν 109654 ▁प्रस्ताव 109651 ▁ІІ 109649 ▁Рес 109649 Nu 109648 ▁Бид 109648 ▁Дзя 109646 lasti 109644 ▁Umar 109644 ▁svarb 109643 ▁onge 109636 ▁सम्बन्ध 109636 ▁इला 109628 ▁Aki 109627 ▁ସର୍ 109627 ▁armas 109626 ▁ikki 109625 ებთან 109623 便利 109623 уреди 109622 ▁thấp 109622 ▁Donosti 109620 yee 109615 ීත 109610 ുണ്ട 109606 ▁suflet 109606 险 109606 eceği 109605 tg 109604 ▁powi 109599 ▁сбор 109597 ▁Vaš 109596 ▁pakalpoj 109596 ሓ 109593 ▁cle 109593 ▁système 109591 क्क 109590 योग 109588 ísticos 109587 ▁самым 109587 ູດ 109584 ▁venster 109584 ▁tê 109583 ▁stessa 109581 过程 109578 ▁Ця 109577 ▁kiti 109569 ▁ezzel 109568 ▁korte 109567 fér 109564 ▁zbor 109564 ▁quase 109563 ▁frequ 109562 itario 109561 ▁kutil 109558 ະນາ 109556 ▁дэв 109555 ▁berkaitan 109552 ▁mər 109547 დომ 109546 лдэг 109544 ▁골 109538 ▁ապահով 109532 事を 109531 ▁Werk 109529 ▁march 109528 ▁žod 109526 ΡΙ 109524 ▁bilim 109523 ่ะ 109520 ▁ularning 109520 ▁семина 109520 ▁Mug 109518 ▁oven 109518 тъ 109512 ▁nola 109509 भू 109507 ▁razum 109505 acağı 109504 ▁конфли 109497 ▁кимә 109496 ▁Tob 109493 ▁fent 109493 ▁lässt 109490 ▁yaşad 109485 פיק 109484 ijat 109481 ลับ 109481 ▁1959 109481 bec 109476 iach 109474 ▁сезо 109468 ▁Blick 109464 ▁Kik 109464 icament 109461 ▁սպ 109461 ▁바랍니다 109458 ▁സ്ത്രീ 109456 ▁hendes 109450 ▁रोग 109449 িদ 109448 ▁មុន 109447 ografie 109443 วันนี้ 109443 ▁שלהם 109440 ▁nenos 109439 ▁notamment 109439 なんて 109429 ▁Реги 109428 ▁nối 109421 र्ष 109415 ▁žem 109413 ▁zrobić 109412 ▁počíta 109411 ▁થો 109410 tari 109409 hlang 109405 ▁жили 109404 平均 109404 ▁Ді 109403 ▁แบบ 109402 amiseen 109401 लग 109401 ▁অনেক 109401 ▁listopada 109398 ▁nätet 109397 ფას 109396 ▁pyr 109396 ▁ય 109394 ภู 109391 ğunu 109388 lott 109387 ▁agdama 109387 ▁práve 109383 ▁निर्देश 109382 erien 109380 ülés 109380 ▁endur 109378 ▁ktoś 109377 ילת 109375 ▁Макси 109375 ▁muta 109370 ▁skaid 109363 ▁2001. 109362 ▁tipu 109362 荣 109362 ▁assol 109361 ▁впечат 109361 ▁икономи 109359 ▁поред 109359 पो 109358 akoa 109356 entzi 109354 lisi 109353 ▁qualsiasi 109353 vinn 109351 發現 109351 ▁Ster 109345 ंगी 109340 ▁TIL 109340 えない 109336 рея 109333 ▁uJehova 109332 راط 109331 ▁cooper 109320 ▁daarvan 109314 ▁շնորհ 109314 क्का 109310 ▁баг 109307 ▁емо 109305 ▁cieka 109304 ▁myö 109301 ▁pne 109294 ▁gamin 109290 ▁भनेर 109288 ktan 109281 omar 109279 ▁Комп 109279 ▁Wikip 109269 llun 109267 ΡΟ 109266 ▁(@ 109264 ▁сексу 109262 ▁съвет 109260 ▁μπορούν 109259 ▁crianças 109255 ▁જ્યારે 109254 ████ 109253 rhe 109252 ▁ostr 109252 ▁дъл 109251 ffel 109250 ▁යව 109250 ▁correu 109249 ▁1971 109248 ▁Kõik 109246 ▁التى 109245 ▁вку 109243 ▁perfekte 109241 ທຸກ 109237 ინდა 109230 ▁håller 109226 ▁push 109226 ▁rossz 109223 ▁obl 109219 ▁przepis 109216 ▁ทาง 109214 ▁بتوان 109210 ▁vengono 109207 ▁confort 109206 ของเรา 109205 ▁sejarah 109204 ▁Ɓ 109204 ▁خور 109197 тип 109194 ▁respekt 109194 ▁כולל 109192 ▁مولانا 109191 ▁laman 109190 ▁Schoul 109188 ที่คุณ 109187 хову 109186 ▁life 109186 ▁ktorých 109185 ▁hero 109176 ▁jashtë 109174 ▁bekas 109173 ئا 109172 ಹಾರ 109163 лежи 109161 альной 109160 ▁calquera 109159 ▁ضلعو 109159 unay 109157 িনি 109157 นอน 109157 ▁לפר 109157 ▁밝혔다 109157 ▁yorum 109155 ▁Nederlân 109152 ▁идея 109151 ▁selamat 109149 ▁bira 109147 umine 109144 ಡೆಯ 109142 ▁33. 109142 raad 109141 ▁înainte 109132 ▁lemosin 109129 ございます 109127 ▁dea 109123 ገኘ 109119 ORG 109117 terer 109113 ▁потвр 109113 ▁mex 109112 ▁καλ 109107 ▁kadib 109104 ▁tehd 109102 ▁нали 109102 ▁компьютер 109097 作業 109097 තුමා 109096 ▁istinadən 109092 ▁समूह 109088 ෙන්න 109083 ▁ጀ 109083 ကု 109079 ▁celé 109078 ▁sọ 109078 মে 109077 ▁than 109076 좌 109075 ▁daftar 109074 ভা 109073 ynyt 109070 ້ມ 109070 ▁seconda 109070 ionen 109062 ▁ମୁଁ 109058 ▁теме 109054 ▁lire 109053 wyth 109051 ▁audi 109048 ▁ellát 109048 픽 109047 ▁Paano 109045 ▁وعلى 109042 となる 109042 rema 109039 дви 109038 ” 109035 ▁1900 109035 ▁Gesetz 109034 ▁alvor 109031 ▁लो 109026 ویه 109021 ▁Rex 109021 ▁kysym 109020 ▁ពួក 109018 ▁امیر 109016 улан 109014 ▁saq 109011 ▁Viz 109010 ▁храна 109009 ▁akus 109005 spill 109001 ▁אש 109000 ▁shahar 108997 લમાં 108996 ▁Zagrebu 108995 ▁аре 108990 мад 108988 ▁plein 108985 ▁രാജ്യ 108984 ▁interne 108981 ទ្រ 108980 iyanın 108979 ▁keinen 108978 ▁украин 108978 կա 108975 ▁රියා 108972 omshuwalense 108971 ▁ಸಚಿವ 108970 kle 108969 ▁Ошо 108969 ▁dalej 108968 ▁НИ 108968 ▁Рә 108966 ▁hoof 108963 ▁stipr 108963 安排 108962 ▁Ние 108960 embu 108958 aghị 108957 ▁ທັງ 108957 owski 108954 omaa 108953 ▁nesp 108953 ▁نشر 108953 ของการ 108949 ắp 108948 ▁stř 108947 ▁práci 108946 ▁ọtụtụ 108945 ▁obilje 108941 ▁Fig 108940 ▁ശ്രമ 108939 තියි 108938 ▁Strat 108937 ▁producten 108936 ▁зарабо 108936 နက္ 108935 ▁тебя 108934 ▁მუ 108932 ▁fæ 108930 ▁лидер 108930 ▁parker 108928 чая 108922 ग्र 108917 ▁Comune 108917 HS 108915 ▁éve 108909 ▁വഴി 108908 ▁ආණ්ඩ 108908 дарҙы 108907 ▁Информа 108906 okana 108904 ▁hót 108901 ▁состави 108901 죄 108900 ▁byly 108899 ▁tööt 108899 ▁dashur 108898 ▁تقد 108896 ▁minun 108893 ▁mặc 108893 ▁amás 108890 ▁samot 108889 ▁витра 108888 ▁утвер 108886 ▁Ajo 108878 ▁kaikk 108876 erings 108870 rota 108867 urnos 108866 ▁rör 108865 ▁засаг 108865 standen 108864 意见 108863 ▁Peri 108861 dera 108860 ▁زندگي 108859 ▁hyväks 108857 ▁Тут 108857 ▁दोनों 108856 ▁મુખ્ય 108856 ▁sels 108855 ▁Technology 108851 ▁вино 108851 ▁xeral 108850 ▁antaa 108849 ▁savait 108849 aktadır 108847 əkl 108838 ▁Selle 108835 တွင် 108831 ▁berjocht 108831 ຽບ 108830 ▁dünyada 108829 ▁iel 108829 ▁Kõ 108826 ▁이제 108825 ▁mapas 108821 ▁cikk 108817 ▁hell 108816 álna 108814 ▁luego 108814 forsk 108812 ্লাহ 108812 legar 108809 ոչ 108809 nalis 108801 ರಿಕ 108796 ▁diwedd 108796 ▁vostra 108795 ▁maidir 108794 ansı 108792 ▁tradicion 108792 usios 108788 ▁varat 108787 ▁драма 108785 nell 108780 説 108779 ▁enak 108778 ▁Seit 108777 ▁בעלי 108775 ▁қолда 108771 ▁Chineke 108770 ▁некоторые 108767 ▁Gabriel 108764 כטן 108762 ▁gép 108757 ميع 108756 lərlə 108750 ઓને 108750 ▁construcción 108750 hyd 108742 ربی 108742 insu 108737 ▁tadbir 108737 ▁бизнеса 108734 меры 108728 ▁law 108726 जना 108724 ▁وبلاگ 108724 ▁нэгэн 108719 ▁warm 108718 ▁시장 108716 zọ 108713 ▁strukt 108712 reas 108708 ICO 108706 ▁дагуу 108706 ▁edifici 108699 deen 108690 ▁Bagaimana 108690 ▁վարչապետ 108689 ▁କରାଯାଇ 108689 ▁شهید 108685 κές 108681 ▁환경 108679 isii 108678 қса 108678 ▁шә 108678 ▁асу 108676 ▁μιας 108675 ▁zein 108674 ▁rasp 108669 ▁godzin 108666 ▁pata 108665 ጥር 108662 ▁ቃ 108659 談 108658 यन 108657 ▁непри 108655 ▁መለ 108654 رایش 108653 ୁଛି 108652 udala 108651 տում 108650 ▁amas 108649 ▁نالو 108648 šs 108647 ▁Reform 108646 ▁extre 108642 ▁planet 108641 ▁fhèin 108640 ▁الشيخ 108638 okot 108637 ▁процент 108636 ▁ishtirok 108627 ▁громадян 108625 ▁Messi 108624 לב 108620 ▁umi 108619 Qué 108615 jeme 108613 ▁geni 108613 lur 108611 prost 108607 ടുത്തു 108607 orni 108606 ▁použ 108601 owymi 108598 ▁kerran 108595 Gu 108592 入れ 108586 চ্ছে 108584 ▁Temper 108580 yoo 108578 ▁Hướng 108577 ecam 108573 ▁gréng 108571 ▁Ngu 108569 ▁wieku 108569 erra 108563 ▁entret 108563 czki 108561 ▁komfort 108560 ▁skuto 108554 ▁ميل 108551 ▁발표 108551 edot 108547 ▁zelfs 108546 tad 108537 ▁δρό 108537 ってる 108536 իգ 108535 релгән 108534 ▁disponibile 108534 ▁prija 108534 ▁сове 108533 ▁رفع 108533 ▁sök 108532 ▁conclus 108529 ▁belül 108528 ▁kest 108523 刷 108523 zten 108519 ▁termen 108516 ▁مليون 108516 ▁महि 108514 ▁jude 108511 ▁därför 108510 ើរ 108507 ▁Ital 108507 ▁sagte 108507 ▁χρον 108504 чиво 108502 jist 108501 ▁tambahan 108499 ▁১৯ 108498 ▁nyilván 108494 媒 108494 私の 108493 ▁शु 108492 ▁ETA 108487 ▁иҫәбен 108486 らい 108486 ▁Dalka 108480 ▁нисә 108480 ▁مثلا 108480 ▁жылдың 108475 рну 108471 ▁Органи 108470 ▁Lem 108468 ▁veľa 108468 ▁krijgt 108467 ▁अनुभव 108467 ⠀ 108467 ქმ 108466 štev 108464 ▁ආකාර 108463 ▁աշխարհ 108462 λων 108461 ▁товар 108457 ▁хэр 108456 ድረ 108454 ▁зона 108453 логии 108448 સ્ત 108445 ▁አሳ 108444 ▁loi 108443 huset 108442 ▁وزارة 108442 ▁интересно 108439 Ni 108438 nahmen 108438 jata 108436 tedy 108433 ደም 108433 ▁xuyên 108431 endal 108430 ▁droga 108428 ▁saud 108428 ▁sure 108428 enschaft 108425 ▁yhdist 108425 ▁إي 108423 ▁fà 108422 ▁део 108422 fiques 108421 ▁байрам 108420 ▁penggunaan 108419 ▁nouă 108418 ▁buli 108411 ▁מוש 108409 СКО 108408 ▁colaboración 108407 Point 108406 한국 108406 oji 108405 ▁меда 108404 .”— 108399 객 108399 改变 108396 enau 108392 ▁bedo 108390 ▁precios 108390 Star 108389 ▁പിന്നെ 108387 랜 108385 ▁තරග 108384 imisele 108381 ▁stock 108379 ▁Bukan 108376 ▁آسان 108372 ▁следе 108370 ▁skuld 108368 ▁čl 108364 `` 108363 ▁Rol 108362 ▁Jana 108361 ▁ভালো 108358 弟 108356 ▁octobre 108355 ▁passo 108354 成了 108353 လုံး 108352 ராஜ 108351 VER 108350 ▁remon 108350 ▁العم 108350 ▁imamo 108349 ▁کلیه 108347 ικαν 108345 κολ 108343 ▁χρει 108342 ▁حقیقت 108335 ▁næste 108332 ▁ezimbili 108331 χουν 108330 ▁100.000 108330 ▁Report 108330 ▁riktig 108327 ▁моз 108327 ▁reja 108323 ▁JO 108322 ▁भिडियो 108322 ▁العق 108318 ुरु 108317 蒙 108317 ങ്കിലും 108313 ▁אירו 108312 ▁сығыш 108309 ński 108307 ễn 108307 ▁etab 108303 ▁सके 108303 yeen 108300 ▁Tässä 108299 ▁아닌 108297 мані 108296 େତ 108296 ▁سیمه 108292 ▁Samm 108289 ▁ናቸው 108289 大変 108289 ▁alcune 108285 iyal 108284 Min 108283 ▁Weiter 108282 ຂ່າວ 108281 otoj 108280 ▁poza 108279 ▁termine 108278 ▁näiteks 108277 ▁nästan 108277 ▁jūsu 108275 ▁କୌଣସି 108275 සට 108272 ▁облысы 108272 袋 108270 muar 108268 ▁ansanm 108267 улы 108266 ▁τρό 108265 ▁අනි 108264 ▁eiro 108261 vale 108257 -. 108256 ൂഹ 108254 ▁skul 108253 ələrə 108249 ▁demok 108249 ترنت 108248 wijs 108247 ▁сіль 108246 ▁fau 108245 ▁klok 108243 ▁cuno 108241 ▁venda 108238 ▁Adri 108237 Pierre 108236 නායක 108236 ▁ομά 108236 াকা 108235 สู 108235 ▁Syl 108235 ▁אתרים 108231 ماع 108230 ▁난 108225 ván 108224 ▁ään 108222 ▁məqsəd 108217 ▁месца 108217 ▁штра 108217 턴 108217 ▁gegevens 108208 ▁ቃል 108202 дож 108199 qinisekisiwe 108195 一定要 108195 ▁interneta 108194 ▁၅ 108193 ▁غني 108192 料理 108192 ▁getir 108191 ▁tule 108191 otgan 108189 ▁Með 108189 ▁Davlat 108187 ▁goberno 108186 ങ് 108185 ▁Skal 108184 ▁kleinen 108183 ▁kropp 108182 ▁санаа 108182 ekommen 108180 ▁wêze 108179 ▁पूरी 108178 成長 108178 ▁González 108176 íst 108170 ▁madax 108169 ▁nutri 108169 افع 108167 ▁አላ 108167 ▁жени 108164 juta 108162 ▁vəziyyət 108158 ゲ 108150 ▁maaf 108149 ▁कमा 108149 ▁aconte 108147 ▁méiglech 108147 vena 108146 ▁neod 108146 ▁Yun 108144 ▁περιοχή 108143 盖 108142 ▁jot 108140 ▁meðal 108139 (1) 108138 ▁ওয়ে 108138 起こ 108135 istel 108133 וזי 108132 ▁Dëse 108130 ▁ਪੁੱ 108129 ▁සඳහන් 108128 ▁squ 108126 ▁użytkow 108125 ▁srd 108124 ช่อง 108121 скер 108120 რობის 108119 雙 108118 ▁קוק 108117 ▁dda 108116 ▁güç 108112 ורית 108110 ▁đỏ 108110 ▁Гер 108108 мәгән 108106 itys 108104 mec 108104 ភ្ល 108100 ▁queremos 108098 ▁ମିଳି 108098 вано 108096 ▁maiores 108095 ▁kojeg 108090 يدي 108082 ▁nevo 108082 ▁Советы 108080 ▁posso 108079 ▁abaixo 108075 НЫ 108069 μπο 108066 页 108065 ▁முழு 108063 ូច 108062 wanga 108061 ▁cambiar 108061 Mas 108060 ▁merr 108057 ▁மேலும் 108055 ▁jota 108053 ▁الوطني 108049 കാല 108045 gün 108044 adien 108039 ▁espacio 108038 ▁Beberapa 108035 俄罗斯 108034 ▁Tug 108032 ▁müvafiq 108032 ▁אָבער 108029 ▁മാര് 108026 ▁osn 108022 ▁நூ 108020 ukun 108018 හිර 108018 дителя 108017 bangan 108014 ahas 108013 ρίζει 108008 ▁beda 108006 ▁klubu 108006 වාද 108005 ▁ျမ 108002 ▁potrebbe 108000 ▁ສົ່ງໃຫ້ອ່ານ 108000 ▁Hanya 107999 ▁билээ 107999 tuja 107996 බා 107996 ▁afaka 107995 angkap 107993 ▁oppr 107991 ▁eisen 107987 ▁Core 107982 ▁Bri 107978 UBL 107977 화를 107976 İZ 107975 值得 107975 egel 107970 kiä 107964 ertu 107963 igor 107963 ٿي 107960 はず 107959 ▁engu 107958 ▁wr 107958 ▁Shir 107957 ▁equipe 107955 ▁диску 107954 ▁پير 107950 ▁zakresie 107948 ▁laste 107945 peri 107941 ▁избори 107939 ▁poved 107938 ▁ਸ਼ੁਰੂ 107938 举行 107938 ▁Проект 107937 ▁ମହ 107935 ▁serão 107930 ▁Wasiirka 107929 ▁үтте 107926 ▁پيا 107926 véd 107925 ▁сможете 107919 arab 107914 ନ୍ତା 107914 ▁عبدالله 107914 ▁αποφ 107907 ▁vör 107901 ▁ሥር 107901 Հայ 107899 ▁структура 107898 ▁міль 107895 Inter 107893 ▁выя 107893 troll 107889 ສັນ 107889 ▁salam 107889 ▁Én 107888 ▁દો 107888 शील 107881 ▁етәксеһе 107881 ▁fwy 107879 vész 107871 ▁грађа 107868 ßer 107865 ▁бүл 107865 成果 107865 الن 107864 ▁siswa 107864 ලක් 107863 ▁เข 107861 ▁ukuqonda 107859 ▁eksk 107858 において 107858 ▁servei 107852 2019 107851 пова 107847 ▁СУ 107846 ▁آئے 107842 ▁دوا 107839 empela 107837 ▁vino 107837 ▁کبھی 107835 рили 107830 эрг 107827 ▁konsult 107826 سام 107825 ▁가족 107825 ᅲ 107822 ▁safle 107821 ▁şeh 107820 ▁ART 107815 ▁מעל 107814 ▁الأن 107811 yrë 107810 ▁prostu 107809 škos 107807 ▁එයා 107807 寻 107806 tifikat 107805 ▁Сен 107796 ▁суда 107795 ▁ခ် 107795 ▁ዘንድ 107792 制作 107791 ▁Божи 107785 ▁уңы 107783 ולות 107780 えます 107779 ▁Lemb 107777 طق 107775 ▁Sach 107774 ▁Χρισ 107774 ▁kater 107770 ▁político 107770 ▁participa 107769 ▁förstå 107764 árov 107763 idda 107762 ЊЕ 107762 侵 107757 riff 107754 ▁분석 107751 ▁служби 107750 veld 107749 мог 107746 antsi 107741 htar 107741 ▁ଗା 107739 ▁perang 107738 ▁anteriores 107732 ▁දුර 107728 ▁වෙයි 107724 גא 107723 нава 107720 ឱ្យ 107719 ▁Holland 107717 ▁ikast 107717 ▁mega 107716 ▁zwee 107716 ▁tilbyder 107710 ▁ಯಾ 107706 urra 107705 ▁perform 107704 ▁Steve 107701 ▁perb 107699 ▁शो 107698 ▁keter 107697 ազմ 107696 ▁nasional 107694 ▁Eig 107693 ▁МЕ 107691 білі 107687 ▁aspir 107687 ▁البحث 107687 ...» 107686 atna 107683 ▁cere 107681 χώ 107680 ▁ଲି 107680 练 107677 лына 107675 دل 107675 ▁rrug 107666 ▁aten 107665 berry 107664 ▁Björ 107663 ▁gruppo 107662 ▁bolig 107660 ▁verst 107659 ▁موارد 107659 ▁ಪಿ 107659 새 107659 ▁امیدوار 107656 ▁desain 107654 ▁ơn 107654 ▁avulla 107650 ой 107649 icil 107647 вље 107647 ▁membuka 107645 ▁скри 107644 ▁bermanfaat 107643 illig 107640 ätte 107640 mania 107638 ▁thảo 107636 を感じ 107636 ▁২০১৭ 107632 ▁kwaliteit 107630 leit 107629 ▁રહ્યા 107629 ▁surad 107625 ▁меньше 107623 ▁Vig 107618 ▁recente 107617 ▁reden 107615 annin 107612 ▁말했다 107611 ਦੇਸ਼ 107609 ▁تکن 107608 ▁რომელსაც 107607 ▁Suom 107606 ▁нао 107605 kanı 107602 ▁muntu 107598 ดาว 107597 ▁dél 107597 ▁جسم 107597 elekile 107596 ətin 107595 ▁обучение 107594 све 107592 nement 107588 ▁በም 107588 ▁құрылы 107587 ▁aim 107585 ▁predvsem 107582 ಲೂ 107581 ▁Hitler 107581 ▁ngờ 107581 šego 107580 ਿੰਦਰ 107579 ▁pemimpin 107575 ▁Сегодня 107575 ▁conci 107573 ▁miasta 107572 ्रेस 107570 ▁збор 107569 useum 107568 ▁mjini 107568 illeadh 107564 ▁paga 107564 ▁prezenta 107564 ▁Magyarország 107563 ოლოგი 107562 ▁Ostat 107559 ▁ਕਾਰਨ 107557 ▁ibe 107556 ที่สุดใน 107554 ▁shacabka 107553 ▁میری 107552 ▁മറ 107552 זע 107551 intsha 107547 ۀ 107546 গর 107546 ▁desig 107543 มากมาย 107541 ▁žmonės 107541 ▁لائن 107539 ▁nepre 107535 veç 107532 jemy 107528 stilling 107525 ▁semplice 107522 ▁تجربه 107520 ▁।’ 107519 ▁Ugu 107517 ▁jaki 107517 ▁կողմ 107517 ▁गुण 107513 に関する 107512 严重 107512 ▁ölkə 107510 ▁поскольку 107505 ▁bilin 107504 ื้อ 107498 ▁уры 107498 ▁araştır 107493 ▁Держа 107493 ▁hạ 107491 ▁view 107489 प्रति 107484 ▁développement 107484 ▁കഴ 107478 enä 107477 ▁Nya 107476 ▁потен 107475 lenül 107473 ūtu 107470 ատես 107468 ▁булһа 107467 ▁heshi 107466 ▁vilka 107463 ▁മുന് 107460 诉 107460 ilaan 107455 ovom 107450 ګه 107448 ▁Αρχ 107445 роне 107443 ésre 107439 вший 107437 ▁Review 107437 трі 107435 ▁sata 107434 ycz 107423 ình 107423 ▁무엇 107423 震 107420 цеп 107417 ▁پایگاه 107417 ▁ارزش 107415 ▁OYO 107412 ▁quanh 107412 вей 107405 ▁пово 107405 рергә 107401 uros 107400 ▁kỷ 107393 ▁Trat 107390 festival 107387 خصوص 107385 ▁producto 107384 ▁ඇත්ත 107384 ▁сынып 107381 ▁effettu 107378 olig 107376 uecht 107376 ▁buh 107376 diyi 107374 ▁ҡатна 107372 ▁swojej 107369 roq 107365 ▁wara 107358 рә 107357 ▁מור 107357 ናገ 107356 ethi 107353 நி 107353 ▁Vale 107353 ▁prijatelj 107351 ப்போது 107347 ▁амь 107346 ▁жақсы 107345 ottság 107343 ƏR 107343 ▁جتي 107337 unha 107333 ▁Νο 107330 12, 107329 -1. 107327 ▁զգ 107327 ▁mød 107326 ▁зор 107323 ▁самое 107323 ▁iri 107322 ▁modelos 107320 ▁actividade 107316 ▁Version 107315 寒 107314 ▁asist 107313 wrs 107312 ▁atmosfer 107307 ▁فیس 107305 ▁okuding 107304 ▁같이 107304 ▁Consult 107303 awatan 107302 ▁WW 107302 utter 107299 ▁praat 107297 tog 107294 ▁رابط 107294 ▁funkcjon 107293 ▁amerik 107292 шня 107288 ▁kategóri 107288 볼 107288 ▁programm 107286 okong 107283 water 107283 ▁cien 107283 ▁výhod 107283 ▁Wanneer 107282 ▁prensa 107282 ▁ჟ 107281 ذار 107279 ▁116 107277 ▁তাদের 107274 ▁соба 107270 ▁خبره 107269 ▁शै 107268 ▁Joe 107267 โอกาส 107265 ▁takut 107265 ▁последните 107259 ▁दुर् 107257 ერა 107256 ▁ຈີນ 107253 ▁Cymraeg 107252 ▁Extra 107250 ▁підготов 107250 inātu 107248 ▁parang 107248 czyć 107247 ▁ТВ 107247 ▁omoguć 107245 ▁kalangan 107242 ▁storie 107242 ▁kitap 107240 ▁lihtsalt 107236 ▁യാത്ര 107236 ▁isda 107235 tant 107230 ▁Сайт 107229 ▁માર્ 107229 ustan 107223 ▁invol 107222 ▁tón 107221 ▁żad 107220 ▁lieto 107219 败 107217 ▁ars 107216 ▁неа 107216 συ 107214 ▁jazz 107214 մբ 107212 ▁மக்கள் 107211 terior 107208 ריט 107208 ▁lux 107207 тую 107202 guar 107199 ▁wame 107199 րկ 107195 ▁zdarma 107195 ಟ್ಟೆ 107194 heits 107188 ▁1,7 107188 ष्टि 107185 ▁Skat 107185 ▁cool 107185 ▁MOD 107184 ▁Alap 107183 ▁filmi 107183 ▁amelyek 107181 ▁habang 107181 ▁Cai 107180 arro 107176 rið 107175 ▁iman 107172 elisa 107171 ផ្ទ 107171 ▁школы 107162 ▁kabukiran 107161 dating 107156 လိ 107156 egno 107155 ▁hurtigt 107155 ▁അറ 107154 ▁ума 107146 ▁drink 107145 ▁قرب 107145 ▁Adi 107144 ▁mì 107143 രീ 107141 ▁باور 107141 ▁Hob 107140 スの 107137 řit 107131 ▁sira 107123 ▁বাংলা 107122 oração 107120 ▁štud 107119 ▁ხოლო 107116 ေပါ 107114 غير 107111 ▁pori 107111 icina 107109 парта 107108 šenja 107105 ▁söker 107101 식을 107100 amuzi 107099 Ur 107096 িয়ান 107095 ▁والوں 107095 ▁пъ 107094 ▁σκ 107093 ▁suku 107091 ▁feil 107085 ▁predsta 107080 ▁teljesen 107080 piecz 107079 ▁geliştir 107078 ▁hê 107078 ▁adm 107075 ▁ភាព 107075 nieki 107073 ▁kjent 107072 までの 107072 ▁آمده 107070 ▁betre 107063 ▁нашу 107059 ▁саҡы 107059 ▁ਵਾਰ 107058 štvo 107057 ▁ees 107050 ▁rota 107049 ▁продукции 107043 ▁ታሪክ 107043 ▁sufici 107040 ÇÃO 107036 VD 107035 ▁ລົງ 107035 uston 107031 ▁посте 107028 意义 107028 كثر 107023 ▁inhl 107020 ▁residen 107020 Ди 107015 ▁konkrét 107014 ▁Klass 107011 ▁ძი 107011 ▁Πολ 107009 ▁жеткі 107007 ▁መር 107005 ▁patron 106998 ▁nombreux 106997 נוע 106995 ▁уси 106995 ოში 106993 ▁ბრ 106993 cký 106991 ▁sjek 106989 ▁Франция 106988 ▁גור 106988 ▁Parad 106987 فضل 106983 ▁Terima 106982 ▁udział 106981 шната 106979 ▁составляет 106978 ▁मेरा 106978 ▁उनका 106976 кій 106972 ▁חד 106972 ▁tý 106971 ▁vilken 106970 mü 106964 ▁остро 106962 стор 106961 לם 106959 ▁неговата 106956 ▁Francis 106954 ▁fitness 106954 ▁קע 106954 wng 106944 ▁stark 106943 ▁sakin 106941 ▁saken 106940 台湾 106939 дного 106937 ੋਗ 106936 ▁tiểu 106936 iction 106932 ▁മനുഷ്യ 106931 ▁Sarah 106930 ▁manokana 106930 ▁دائ 106930 ▁tank 106929 ▁noz 106923 ▁связа 106922 ▁impro 106921 ută 106920 צת 106920 oliko 106917 ▁udhë 106917 ាជ 106912 ▁уровень 106909 ▁ରୋଗ 106909 ▁bisk 106907 ▁mch 106900 зује 106897 ▁voljo 106897 ▁등의 106896 ▁yaşlı 106893 ▁ezint 106892 hånd 106886 ▁Lugo 106885 ▁Shah 106882 それは 106881 ▁Ning 106877 ▁مض 106875 ▁vile 106872 ▁адре 106870 ▁לשל 106869 ▁ලොකු 106867 ხის 106866 ▁tę 106865 ▁қатар 106865 ▁sman 106864 ලී 106855 ▁cysyll 106854 ▁osallist 106854 ULI 106853 ▁ndio 106853 ukku 106847 ▁побу 106847 ▁הן 106847 ▁Жаңа 106845 vēr 106842 ရက္ 106842 ▁deireadh 106839 ▁ਦਿੱਲੀ 106839 ▁Services 106838 ▁кварта 106836 ▁choć 106832 ▁valmis 106832 čnim 106828 ئون 106825 ውል 106823 manı 106821 လမ္း 106819 embra 106813 ਦੀਆਂ 106810 ▁gis 106802 ▁ይችላ 106802 ▁λογ 106800 із 106798 ▁Mkurugenzi 106797 ▁naziri 106797 яв 106796 ▁lån 106793 ▁dermed 106789 եղծ 106788 高校 106787 ▁Saudi 106784 ▁fleiri 106780 pò 106779 рено 106774 ▁thao 106774 ▁moderno 106773 ОВА 106756 מנים 106753 蘭 106751 ësia 106750 ▁pinag 106749 ▁dikt 106748 ▁puhul 106747 ▁ביום 106746 ▁ന്യൂ 106744 indawo 106742 ▁Paling 106742 ▁зміни 106742 ENC 106740 ▁සට 106738 初めて 106737 ▁cak 106730 chef 106729 owied 106728 ▁Ami 106728 ▁nyelv 106728 ▁maji 106726 ▁hanggang 106723 днее 106720 ▁Match 106719 ▁Һа 106718 προ 106717 ▁словам 106717 币 106708 ▁ബ്ലോഗ 106707 ▁brenda 106701 ▁κατο 106701 ιού 106698 ▁ekonomik 106697 prat 106695 ▁éxito 106694 ▁Націона 106692 ▁مرسته 106691 ▁разпо 106690 ▁odv 106685 гаад 106682 ப்பட்டது 106681 ▁сэтг 106679 ▁McC 106671 ▁api 106667 ▁xalqaro 106667 ▁លើក 106665 ▁ов 106664 ുണ്ട് 106660 ▁पूर् 106660 ▁Gang 106658 ▁Ott 106657 ଃ 106656 ▁23- 106654 ունք 106649 íamos 106646 ▁hayang 106646 ▁послу 106646 ▁totul 106645 ▁ଲାଗି 106645 ▁sieht 106637 पण 106635 bilder 106629 ▁እኛ 106629 строи 106627 žan 106626 rejme 106625 ▁tiss 106625 ▁ಕಾರ್ 106622 添 106621 ▁Ourense 106620 ▁suom 106620 ▁xat 106618 ▁vieną 106617 ensemble 106614 ▁നമ്മുടെ 106613 ▁anima 106612 ▁જાણ 106608 селәр 106607 ▁takmičenja 106607 ▁אב 106606 ▁مير 106604 ▁korištenja 106603 ாத 106602 ▁ictimai 106601 ▁denk 106600 ▁আইন 106598 ▁yote 106595 ▁आमच्या 106595 ିଲ୍ 106593 ▁فناوری 106592 buch 106588 ▁latta 106588 ▁לחי 106587 ilí 106586 ▁världen 106585 ▁شرح 106585 ▁sosi 106582 ▁услу 106581 보고 106579 ▁NRK 106575 ▁اجازه 106574 life 106572 ▁gaf 106570 ▁ഭൂ 106569 ▁yek 106568 લુ 106567 ▁حجم 106564 ▁својата 106561 ▁הגי 106557 றும் 106556 ťaž 106555 ▁Cơ 106554 ▁fho 106554 ▁ponieważ 106551 ▁ഇന് 106550 行动 106549 ▁þig 106548 ▁comunica 106547 ▁آماده 106547 ▁kuasa 106546 ▁Elkar 106539 ▁Station 106537 ▁сайті 106536 adienį 106535 ോഗ 106532 ▁Typ 106531 ▁управ 106531 zí 106530 чаць 106530 ▁статисти 106529 ▁ihnen 106528 ▁genel 106524 ▁krv 106524 ▁product 106524 ສາດ 106523 ▁ବ୍ୟ 106520 ▁equilib 106519 ਹੇ 106518 mic 106517 ▁আগ 106517 ▁kadagitoy 106516 ▁nærheden 106516 ▁توانند 106514 ▁نما 106509 jetno 106507 stoff 106506 ▁совер 106506 óri 106503 ▁थप 106498 ▁gadi 106496 basa 106492 ▁ಪ್ರತಿ 106492 执 106489 สวน 106487 ▁ಪುಸ್ತಕ 106487 ▁siêu 106485 -40 106482 ▁свако 106482 čite 106481 μισ 106481 ▁order 106479 reven 106478 ▁vanskelig 106478 ▁zašt 106476 ▁territ 106475 ▁општи 106475 ▁begyn 106473 ▁saga 106473 ▁Vista 106472 ▁περίπτωση 106472 ▁июня 106460 ▁Pulau 106459 باط 106458 ▁Departament 106458 uchen 106454 ລະບ 106454 ▁dinero 106454 тне 106452 ▁moitos 106450 onio 106449 ▁Shqipërisë 106449 ▁폭 106449 volution 106446 ▁cheann 106445 幸福 106438 ▁maamulka 106437 ▁obej 106435 ▁Brazil 106434 arki 106432 มั่น 106430 ▁находи 106430 ▁მაინც 106430 ▁Kön 106428 ערע 106427 ोजन 106427 ▁усіх 106426 ▁DV 106424 ▁wazi 106424 ▁الحياة 106422 ፈለ 106421 ▁најбо 106420 ទទួល 106419 ▁Kredit 106414 ▁გიორგი 106414 ▁قانونی 106413 ▁inclus 106412 ▁GmbH 106407 ▁Reykjavík 106407 ▁不 106407 öf 106406 ▁болов 106401 ▁ब्ल 106399 unin 106398 ▁dimensi 106394 estudi 106393 ▁prona 106393 ▁adlaw 106389 ▁aspect 106385 ▁funda 106384 ▁hada 106384 ební 106379 ▁šį 106378 вних 106375 ▁imati 106374 ▁الوا 106374 ▁heure 106369 ▁будуть 106369 ▁principio 106368 ▁estet 106367 ▁ими 106366 ສົມ 106362 ▁Համ 106362 ▁близ 106360 ຍິງ 106359 ▁cumpr 106359 ▁direito 106357 וצי 106356 رته 106355 ▁áit 106354 ፋት 106352 ▁Prat 106352 Tech 106350 ագիտ 106350 ▁ചോദ 106349 मार 106344 ▁Rug 106342 ṣe 106338 ▁ayrı 106337 hich 106336 ▁chấp 106333 រី 106331 ▁daus 106327 ▁dwóch 106327 ევს 106318 ▁کنترل 106317 ავთ 106315 ▁kiểu 106315 ▁tup 106309 ▁Prad 106304 ومت 106303 ▁ochr 106303 ▁Μη 106302 ▁отда 106301 ▁изиск 106298 ▁ಗ್ರ 106297 ▁egon 106296 ▁ახალ 106294 乘 106294 ずに 106293 jien 106291 edores 106286 ▁предпо 106283 ▁රශ් 106283 ▁geno 106280 ▁conte 106276 ▁compart 106273 зани 106272 ▁exter 106272 商业 106270 ▁Stu 106268 ▁febru 106268 意思 106268 符合 106268 ▁पर्ने 106266 ▁daude 106264 ▁mær 106262 tikas 106259 ▁ем 106259 歯 106258 cyjne 106256 መና 106256 ▁cinn 106256 序 106255 apie 106252 ▁dikenal 106251 ▁cens 106246 ▁अभिने 106246 ▁Jahres 106241 zną 106239 ард 106237 ▁सामान्य 106234 ▁jub 106233 ▁média 106232 ează 106230 성이 106228 ▁maeneo 106226 ▁ох 106225 igli 106224 ▁afa 106224 软件 106224 ▁urmă 106223 meni 106218 ▁Idag 106216 ▁ясно 106213 ▁নিউজ 106213 mén 106211 ▁général 106211 indah 106210 ίσει 106209 ▁वी 106207 ▁siirt 106206 ▁vjen 106206 ଝ 106204 ▁sufr 106204 wii 106202 ▁απέ 106198 电视 106196 ▁Buenos 106195 ▁découvrir 106195 ▁ಕೃ 106195 geus 106194 ▁bakter 106194 ▁спів 106188 aceae 106187 ▁هد 106187 ▁ម៉ោង 106185 ພາກ 106180 ▁Lý 106179 ▁infra 106179 ▁بڑے 106179 ▁action 106173 ▁συμφων 106169 ▁συνα 106167 ▁अभियान 106167 ▁kejadian 106165 ▁samm 106163 ▁insanların 106162 ▁मामले 106161 ▁дис 106158 ▁nuor 106154 ▁ɗee 106148 ▁debido 106143 ▁équip 106140 fører 106133 ▁отзы 106133 ▁6000 106129 ไฟฟ้า 106128 ▁роки 106128 ئت 106127 etoa 106126 ሞች 106124 атра 106123 ▁Massasje 106123 ▁există 106118 ▁가지 106118 ▁BT 106112 ustada 106109 ▁novel 106109 ▁kenyataan 106108 ▁gweith 106105 grade 106104 ტს 106099 ▁کڻي 106098 édi 106096 似乎 106096 ▁synd 106092 ▁Sabi 106085 ▁apresenta 106083 tiger 106082 ▁complement 106082 陸 106076 ▁толық 106074 hiin 106072 ▁xabar 106071 ▁Ș 106070 ▁decidir 106065 ▁نسخه 106064 2), 106063 ▁التعليم 106063 ▁congr 106062 ▁ສ້າງ 106061 ▁hair 106056 ▁jamii 106055 ▁naszego 106047 ালের 106045 তিক 106043 ayakan 106036 맨 106035 ▁लाइ 106034 ▁பொது 106032 ▁месеци 106031 ▁muod 106027 ▁contrat 106026 ▁pikk 106026 ▁Мал 106026 ▁страда 106021 なのか 106018 ▁याद 106017 ▁როდესაც 106017 ▁Európ 106009 мәһе 106008 ▁иско 106006 мены 106004 ▁ënnert 106004 ▁आम्ही 106004 ▁लड़ 106004 វិញ 106003 ▁HS 106003 သမီး 106002 ▁ntango 105997 ▁Cela 105993 ▁klu 105986 ▁síðan 105986 ahaa 105984 pose 105982 нена 105982 ▁kailangan 105982 ▁केंद्र 105982 ▁ଆମେ 105981 lije 105973 ▁bern 105972 ▁חדשות 105971 ▁hogyan 105970 ▁näkö 105967 ekul 105965 ▁Dez 105965 ▁pašvald 105964 τικότητα 105962 ▁dramat 105960 ▁Treb 105958 ▁फु 105958 ▁iliş 105955 ▁EZ 105954 ▁ආස 105954 ▁სო 105948 нули 105947 افی 105943 сток 105942 tian 105937 ▁strany 105936 ▁بھارتی 105935 atzea 105933 注册 105932 ▁Dost 105930 ▁фак 105930 ▁Update 105928 anici 105927 projek 105924 џи 105924 liwo 105922 ▁Slot 105922 ▁kurių 105922 dak 105918 éner 105918 ือก 105917 ្ក 105905 ▁לשי 105905 ▁سمت 105905 ▁۱۳۹۶ 105904 ▁etre 105902 ▁Daerah 105897 ▁acad 105893 ▁કરીને 105893 样的 105892 বস 105890 ເຂົາ 105889 ▁כח 105889 ▁പേര് 105885 cida 105883 ▁muhimu 105882 ▁Trip 105879 ▁cancer 105878 ▁CI 105875 ▁Ovo 105871 ▁日本 105864 ▁हाथ 105863 alama 105860 حال 105858 第一次 105857 itzar 105856 ້ງ 105855 ▁голе 105854 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99249 ▁қам 99245 ▁wom 99244 ቤተ 99243 ▁Kemudian 99243 ▁ويب 99242 ๐ 99238 ▁swydd 99238 ▁регистри 99234 ▁temperat 99233 ▁Politi 99231 ornost 99223 ▁fortfarande 99222 ိုင်း 99220 ▁najlep 99219 cach 99216 ▁Նիկոլ 99216 οπ 99211 ljajo 99209 лят 99209 ▁놀 99208 なお 99202 ▁posib 99200 ▁ihop 99199 βολ 99197 ▁یوې 99196 ugos 99195 ათი 99195 ▁auga 99195 ▁Sva 99194 ▁ඉදිරිපත් 99194 entai 99190 ▁села 99188 就在 99185 amah 99184 ▁Шин 99184 หย 99181 ▁ince 99180 ashen 99179 ▁utrolig 99179 féle 99175 ятся 99175 اهای 99175 ▁קומט 99174 ▁peque 99173 ▁Garden 99171 ónu 99165 ▁marina 99165 ▁பெற்ற 99163 ambu 99162 dob 99159 ▁ұйым 99157 ▁જોવા 99151 ▁свій 99147 ▁матеріалів 99142 ▁Пан 99135 ▁PVC 99134 ▁Dü 99133 ▁misst 99133 ▁збо 99133 ▁Kỳ 99131 ▁mbili 99131 ▁மக்க 99131 ▁иметь 99128 ▁vina 99124 ็อก 99123 ▁관한 99123 ▁цэн 99121 ▁রহমান 99121 ▁Ваше 99117 finy 99113 ▁עט 99109 ▁철 99108 无论 99107 ▁indispens 99105 áticas 99102 ▁siga 99102 ▁zadovolj 99101 väg 99099 ▁Fotos 99099 ▁kasu 99099 tár 99095 ▁parents 99094 ▁գործունե 99094 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98978 ▁උද 98974 ▁técnicas 98973 ▁lös 98972 ▁Themen 98971 lob 98969 ▁YO 98968 ฏ 98967 ▁정치 98967 konom 98965 нін 98965 ▁Gaa 98964 ▁Tarn 98963 ▁վերջ 98963 ▁Mia 98962 ▁fòr 98959 すぎ 98958 ▁орнала 98956 äti 98955 ▁χρήση 98955 ▁പിന്ന 98953 зах 98952 सव 98945 ▁мрежа 98945 ▁яй 98943 ▁לעבן 98941 ▁dand 98937 ▁shpër 98937 ologo 98934 ▁შესაძლებ 98934 ojumu 98932 xel 98932 ▁فوتبال 98931 אני 98930 ▁vaiz 98927 ▁گن 98926 ▁одли 98915 ▁април 98914 ▁بيا 98912 ▁crisis 98911 ▁চে 98911 ▁варто 98910 ▁ଯେଉଁ 98909 טוב 98907 ถม 98907 ▁nuevas 98906 ▁मंगल 98903 eprav 98900 ▁Hund 98900 ▁değ 98900 衛 98900 ▁Georgia 98899 ▁obter 98899 ალო 98898 ▁áb 98898 цэр 98897 วั 98897 ▁مین 98897 ▁rapor 98896 adhan 98893 finition 98890 ▁شوه 98887 bier 98886 øres 98886 就像 98882 ▁nhờ 98880 ▁මෙහි 98880 ▁nuestras 98878 ▁impres 98874 ▁previsto 98873 ▁romana 98871 ▁blik 98869 IKE 98865 idden 98865 ▁कधी 98865 ЖИ 98863 ▁эҙ 98863 world 98860 ▁gastos 98860 ▁그래 98858 ▁uSathane 98856 ብረት 98855 ▁Афри 98855 inand 98854 ▁즉 98854 રના 98852 ▁въпрос 98850 털 98850 ුරු 98848 ▁działania 98845 ▁ním 98844 ▁ጌ 98841 ▁zavar 98840 ▁bella 98839 ▁ופ 98839 ▁Ew 98836 ▁Ինչ 98836 ▁ਕਮ 98827 ላም 98826 ▁خي 98825 ▁आपकी 98825 ▁preso 98823 ▁Hig 98822 тельное 98821 ताब 98821 ខ្ញុំ 98820 ວກ 98819 ▁hled 98815 ▁lány 98815 ▁දේශ 98811 랙 98810 ▁хэм 98808 áže 98807 ▁कृषि 98807 験 98807 ଭି 98804 ▁ҡат 98804 telefon 98801 ζεται 98801 udah 98796 ▁marché 98796 ▁învă 98796 Ậ 98795 берете 98794 ▁salle 98794 ▁lleva 98793 arada 98791 ▁comput 98791 ▁Թուրքի 98789 ▁દિવ 98787 Њ 98785 ▁abra 98783 ဖြဲ႔ 98781 ▁карто 98779 hver 98778 inander 98778 ▁akh 98777 ▁التط 98775 ▁parler 98772 ▁terlebih 98772 ગ્ર 98770 ▁persoal 98770 inao 98768 ▁времен 98767 ▁जात 98767 ▁دام 98766 ilak 98764 コー 98763 йшли 98762 ותו 98761 ିବାରୁ 98759 ▁právě 98757 ▁uke 98755 ▁viņ 98755 ▁Access 98753 টার 98752 दो 98751 ▁Sint 98750 ▁modelu 98744 ▁៦ 98743 Chr 98741 шните 98737 ▁keyin 98736 Don 98731 ▁Ministar 98731 കാരം 98730 ▁agingga 98730 ója 98729 лить 98727 ▁50, 98725 ताको 98724 ▁تاثیر 98723 ▁tāpēc 98722 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කරුව 98577 ▁roa 98577 ▁ગયો 98574 ▁Mila 98569 urance 98565 ▁Johnson 98565 ▁бастап 98562 ICH 98559 janje 98554 ▁estudio 98553 ▁दिला 98551 hiq 98549 ▁pods 98548 ▁бөлі 98543 ▁የግ 98543 ▁atuh 98541 ▁протягом 98541 ▁Ísl 98540 neje 98539 ರನ್ನು 98539 ▁өткен 98539 ▁막 98534 zył 98530 ▁мама 98530 sept 98529 ▁الخدم 98527 íts 98526 сних 98519 ▁живее 98518 គ្រប់ 98516 ▁Ihrem 98514 aner 98513 upheka 98509 ksyon 98507 ▁вза 98507 ▁वाला 98507 ▁სურ 98507 ătoare 98505 ▁Secara 98505 ▁уреди 98504 rence 98503 wich 98500 ▁hónap 98500 ▁разо 98500 ▁לראות 98499 ▁საჭირო 98498 regist 98493 មន្ត្រី 98492 ▁toler 98490 ोन 98489 のでは 98487 сым 98485 ▁თვალ 98485 ቄ 98480 ▁такива 98478 ▁હિ 98478 ▁پلا 98477 ីឡ 98475 walo 98474 ▁тело 98473 ▁ngiy 98471 ▁रेल 98470 ็ม 98465 ▁orin 98465 ▁començar 98461 ▁pensa 98459 ▁seker 98459 ▁Республикаһының 98457 ▁الزام 98456 ந்தது 98443 ▁Оқу 98443 ▁erabiltzen 98442 ▁دیں 98438 ေတာင္ 98437 ▁atsk 98435 ▁жатқан 98435 єдна 98434 ଦ୍ର 98431 ▁කාර්ය 98431 Uviéu 98430 ▁kokona 98430 ▁érdekében 98430 шніх 98424 звичай 98422 ▁зробити 98421 гора 98416 ▁költség 98415 ▁ፓ 98415 uanya 98414 ▁trzy 98414 ▁yash 98412 ▁बढा 98412 itab 98410 ▁efni 98410 ▁சம்ப 98407 ▁?1 98405 らしい 98405 ▁ανάγκ 98404 ғыз 98403 ▁ነገ 98403 ▁soud 98399 တၱ 98396 ▁került 98393 apeut 98388 ▁милиона 98386 ▁ਨਿਰ 98384 ąg 98382 ଜ୍ 98382 கே 98382 ▁ჩვენს 98380 mers 98379 엘 98375 ▁Sut 98374 ▁חש 98373 ფრთხ 98372 ▁klaar 98372 ▁fij 98371 ▁cut 98369 тропо 98360 ▁તમારી 98360 ленә 98359 льну 98354 ▁लगानी 98351 ▁विद्यालय 98344 ПО 98342 ▁ঢ 98342 íomh 98341 ▁никто 98340 ▁жария 98339 ▁1962 98337 ▁ddar 98337 遗 98332 ▁Gwelet 98328 ▁okuth 98326 ▁edilən 98324 ▁útil 98324 ▁теңге 98324 ուգ 98316 ▁опыт 98316 assin 98310 kään 98308 ປະຕິບັດ 98307 inimas 98304 ▁koe 98302 ▁menulis 98302 шире 98301 ▁Oficial 98299 ▁آز 98299 ▁wybodaeth 98297 ▁ہوگ 98297 僅 98297 ▁осъ 98293 ▁conditions 98290 ▁Ně 98289 ▁veri 98289 ▁пенси 98286 ▁ನಟ 98285 zko 98284 ▁autonom 98279 จุ 98278 ▁pane 98277 ▁deal 98275 ▁алкого 98273 ട്ടില് 98272 ▁teme 98270 ▁Major 98267 oties 98265 ▁मैले 98262 ▁традицион 98261 éad 98257 ▁Mant 98256 ▁stroj 98253 ▁3.5 98251 ▁Denna 98247 ▁واحدة 98247 ▁بنیاد 98246 ▁zapis 98243 ▁ποτέ 98240 емы 98236 ▁sahabat 98236 ▁תכ 98233 ▁134 98232 ▁plads 98231 ▁läs 98228 人类 98228 ▁təsdiq 98226 ▁gá 98225 ▁مضمون 98225 ▁ಎರಡು 98222 ▁target 98220 ตี 98219 ▁soen 98218 Av 98217 -35 98216 पक्ष 98216 ▁활용 98216 ▁Gand 98214 ▁Ligi 98214 saw 98212 ▁Notre 98212 ▁ଅଧ 98211 ikud 98205 電子 98205 ဘဲ 98201 cek 98198 ▁anois 98196 uray 98195 ▁najman 98194 ▁batek 98193 ือง 98191 ▁obe 98191 ▁ស្តាប់ 98191 ึ่ง 98186 ▁unterschied 98186 ▁około 98185 ಕ್ಷಣ 98184 ▁carácter 98182 ▁meteor 98181 ▁atmos 98178 ▁innebär 98178 ▁බවයි 98178 ▁وط 98175 ▁ജില്ല 98175 ▁tête 98174 niji 98173 ▁pige 98172 ▁yaran 98171 ovací 98166 ▁dier 98165 ▁ڪيس 98163 系統 98162 ▁пута 98156 ▁términos 98154 ▁бирә 98154 inana 98150 larg 98149 ▁Bár 98148 amą 98143 ▁کودکان 98140 ▁Yhd 98138 ▁babo 98134 ▁pera 98133 ▁комбина 98132 ယား 98126 ▁mkubwa 98125 home 98123 ▁السود 98119 ▁Liber 98118 ▁cały 98117 əsiz 98116 ▁persoa 98115 ▁للإ 98114 ▁konie 98113 ▁ડો 98113 ▁بشر 98109 甘 98109 ▁pirmo 98105 biamo 98101 ټو 98101 ▁markaana 98099 ▁sprac 98097 dad 98096 之後 98095 헌 98095 ruit 98093 ▁яке 98092 ▁талан 98091 ハハ 98088 ัฐ 98087 ▁zákona 98086 ▁apik 98084 ▁Ble 98080 ▁dagoen 98076 ▁Оба 98076 ▁போது 98073 ल्स 98069 ▁vượt 98066 ikkaa 98062 ▁språk 98058 ENE 98052 ▁vanuit 98050 robi 98049 ▁vďaka 98047 мани 98046 ▁sviluppo 98046 ▁poé 98045 เก่า 98044 ▁mén 98044 ▁umshuwalense 98041 ೊಬ್ಬ 98038 ▁તરીકે 98038 ▁points 98036 йтесь 98035 ▁Ալ 98035 werken 98034 ▁świecie 98034 ▁היימישע 98034 Ku 98033 ▁Kast 98032 ▁Kenn 98032 ▁érkez 98032 ▁родите 98032 meti 98031 szik 98031 ▁سيم 98031 ▁gül 98028 ▁elektro 98025 ▁letos 98023 ▁egal 98021 ▁મળે 98017 плі 98016 ▁národ 98016 ểm 98014 ▁tốc 98013 ীব 98008 ษ์ 98000 ▁onderzoek 98000 ▁мәғлүмәттәр 97999 ▁કારણે 97995 ▁113 97994 ▁pály 97992 ▁únic 97991 ▁mB 97990 ▁кня 97990 ▁Трамп 97989 ▁ျပန္ 97989 រ៉ 97988 ▁hafði 97987 ▁AUT 97985 tiounen 97984 ▁Geri 97982 tong 97981 顶 97980 ▁Produkte 97978 alala 97977 種類 97975 ▁سون 97971 тром 97969 ▁laiko 97969 ▁થશે 97969 ▁aðeins 97968 nios 97967 ▁diabetes 97967 投入 97963 ର୍ମ 97962 ▁१४ 97958 ▁compat 97955 ▁sevi 97954 ▁әлем 97952 تل 97951 ▁булараҡ 97948 ▁وڏ 97948 ▁emberek 97947 bundet 97944 ▁שעות 97943 شور 97942 ılmaz 97941 ▁തുറ 97936 ▁प्रमाण 97934 ▁realidad 97933 ▁дети 97933 ▁հաղթ 97933 () 97931 ▁ያሉ 97931 ▁גרי 97930 ekta 97925 ▁Galego 97918 ▁тохи 97915 ိမ္း 97913 inean 97912 ▁тыся 97912 ▁plastic 97911 ിലൂടെ 97910 ▁NS 97907 ▁Ljubljana 97905 ▁پاڼه 97903 ▁საფ 97902 גור 97901 ▁kasadpang 97900 ▁almak 97899 それを 97894 eram 97893 rats 97892 řeb 97889 ▁קבל 97889 ▁stred 97888 lərdən 97886 ▁sợ 97886 年に 97886 บริ 97885 ▁diatas 97885 ▁разом 97885 ଖରେ 97877 ▁हात 97877 সং 97875 ▁ունեց 97873 ▁руки 97871 生物 97870 ▁πολυ 97869 тельным 97865 ▁novemb 97865 ▁Универзите 97865 ▁Bour 97863 ▁shah 97863 ▁sozi 97863 ▁χώρο 97861 ▁књи 97861 ▁रात 97860 się 97859 ▁Taw 97859 сус 97857 ▁отпра 97855 ▁ຕາມ 97855 otz 97854 cire 97853 ▁urrainn 97851 ▁ช่วย 97851 ørte 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95045 ▁ponto 95044 ▁pomeni 95043 ÊN 95039 ▁lė 95031 ▁četvrt 95029 ibona 95025 ▁Narod 95023 ▁කොළඹ 95023 ▁באמת 95022 잡 95020 ouver 95017 ▁wano 95017 ▁કં 95017 ▁Nec 95015 思考 95014 ▁ponovno 95013 ▁colleg 95007 ▁પહેલા 95006 ▁NT 94999 ▁dunya 94998 ▁mitan 94997 ▁განს 94994 ўшы 94993 ηθεί 94992 ▁politici 94992 ▁متاثر 94992 utana 94991 ▁අන්ත 94991 Mon 94990 ▁บริการ 94988 购买 94988 ▁trīs 94987 ▁forint 94986 ▁rozs 94983 ▁agg 94981 שיבה 94980 よい 94977 lő 94974 ▁effekt 94973 ▁માત્ર 94972 edenis 94971 ▁כשה 94971 ▁תוך 94969 נייה 94966 ▁JU 94960 zey 94959 ▁яку 94959 ▁случаи 94956 reng 94953 ▁Research 94952 ტრო 94951 чает 94950 ACK 94948 ▁ਠ 94948 ▁യൂ 94947 ահայտ 94946 ▁باست 94946 ▁daarom 94943 ▁רוב 94943 ▁expli 94940 ▁publicar 94939 ▁प्रधानमंत्री 94936 omez 94934 ଚା 94933 ▁biedt 94933 ▁அதை 94933 தான 94932 -1, 94931 ▁Evrope 94931 лағы 94928 ogh 94927 ▁مرحله 94927 cií 94926 ▁болести 94926 ▁πολιτική 94925 ጥታ 94921 ▁боро 94921 ▁شکن 94921 ▁budeme 94914 是不 94911 ustest 94910 мас 94910 ▁άλ 94902 ▁imatges 94900 ween 94899 ▁ดี 94899 ▁ერ 94895 ▁kwaba 94894 ୋଷ 94888 ΐ 94887 astar 94885 ungkan 94885 edda 94883 ▁sche 94881 ▁obsahuje 94879 ▁pelanggan 94878 ▁cuanto 94877 ከራ 94875 ▁ungakwazi 94874 ▁Дали 94874 ▁واک 94874 ▁ਜਾਂਦਾ 94872 ዝቡ 94870 ួប 94868 ▁realizada 94866 ▁Geni 94864 ើត 94863 ▁relevante 94863 usement 94860 ▁умови 94859 ▁acto 94858 ▁Niz 94857 azini 94856 ▁мотлаҡ 94854 ▁Lod 94851 ▁uJ 94850 ன்னு 94845 视频 94845 ▁моли 94842 ▁traf 94838 ▁않습니다 94838 ▁씨 94837 ▁якой 94836 дних 94832 yano 94831 ▁daging 94830 ▁somm 94828 வரை 94827 ▁emva 94827 ങ്ങിയ 94825 ▁senc 94824 ▁Китай 94824 ▁onlara 94823 ▁balikas 94821 ▁vão 94820 ostos 94818 gai 94817 vār 94817 ակայ 94815 ▁कृ 94814 mă 94811 isses 94809 irithe 94808 ▁ନିର୍ବାଚନ 94808 ಷ್ಣ 94805 oben 94804 ▁struč 94803 ▁bestaan 94800 ▁koskaan 94799 ▁langer 94798 දිය 94795 iação 94792 водле 94790 ▁llei 94789 وران 94788 ▁bringen 94785 දුර 94784 ▁১১ 94783 ▁ያህል 94782 ▁înch 94779 album 94777 gár 94777 ▁еми 94775 iw 94774 ysh 94770 งาม 94767 ▁мөр 94767 ▁жина 94764 ▁अच्छा 94762 ▁ಮಕ್ಕಳ 94762 ▁Wię 94761 ▁ista 94760 поль 94759 ▁ખો 94759 調査 94757 怎 94753 ▁aso 94752 ▁прид 94752 ▁ዓመታት 94751 bali 94749 ▁rék 94749 ▁Gaeltachta 94747 ▁داریم 94747 pov 94742 ไง 94741 ▁Waar 94741 ▁nucle 94740 ▁xorn 94739 ▁funzion 94738 ▁28- 94737 chung 94736 irmar 94735 дыё 94735 şam 94733 ▁exposi 94732 quent 94731 اة 94729 ▁инвали 94725 ▁мена 94725 ▁ajud 94724 ▁isl 94722 க்கான 94720 ▁واشن 94719 iis 94717 brauch 94715 ▁असलेल्या 94715 ▁gitar 94714 ແນ 94713 ៀម 94713 ▁kabisa 94712 ▁lietu 94711 ▁tawii 94711 ▁አያ 94710 ymi 94707 мку 94707 ▁Mano 94706 ਸਾਰ 94705 也会 94705 ிட்ட 94703 ▁võiks 94703 ▁පර 94700 ▁nặng 94695 ▁nuus 94692 ▁Ski 94691 arbeitung 94685 ▁Ents 94685 ▁ideje 94680 ▁nnyo 94679 ▁അതു 94677 ectivos 94675 ▁Akhir 94673 ▁अदालत 94671 abis 94669 umesc 94669 ▁μισ 94663 จะได้ 94661 ▁ಸ್ಪ 94658 ▁kadag 94653 تين 94651 ▁филосо 94651 ▁уверен 94650 ▁Mò 94647 ▁наблю 94645 ▁Μπο 94641 ▁mulleres 94640 ▁készült 94639 竟 94638 ▁tane 94634 škų 94633 ▁Skype 94633 ▁terenie 94633 ▁അയാ 94633 ▁Какво 94632 ▁гран 94630 ▁dicembre 94629 ▁Tudi 94628 ▁essay 94626 ishing 94625 торов 94624 ļau 94623 報告 94623 ▁Хан 94622 重新 94622 uis 94620 ▁turvall 94620 ▁štát 94619 EKT 94617 印度 94617 ▁неф 94612 ▁kungaba 94609 ليه 94607 ▁română 94606 ihen 94603 ▁botên 94603 ▁pedi 94603 ▁spēl 94603 ▁message 94601 ▁үткә 94599 ▁मुलुक 94599 ▁නොව 94597 ▁உள்ளது 94596 ුර 94595 itzat 94592 वेदन 94592 ▁ದಾಖ 94588 ន្ន 94587 ▁քննարկ 94586 ജീ 94580 ửa 94577 สัตว์ 94575 чених 94574 出口 94572 表現 94572 ▁progres 94571 ▁Kib 94569 ▁wegen 94569 ▁ከማ 94569 omor 94568 slov 94568 ▁devlet 94564 ▁instruc 94563 ▁mətbuat 94563 ▁ауылында 94561 ▁ಕ್ 94561 ▁חדר 94560 ▁полез 94554 Ти 94551 ▁wyda 94550 腦 94547 ▁SOC 94546 我想 94545 ровки 94543 ▁egna 94542 ▁magni 94542 ▁теку 94540 ▁түрде 94540 נגען 94539 ▁době 94539 ▁Departamento 94538 ▁нових 94538 ราว 94537 ẩm 94537 ▁කලින් 94537 əng 94535 ▁sepanjang 94533 ປະທານ 94528 lənd 94527 ЭЛ 94525 英語 94523 ▁Здесь 94519 ▁បទ 94518 եի 94514 ▁meghatároz 94514 ▁ବିରୋଧ 94514 ▁Assist 94513 өш 94510 وندي 94510 ▁tilgang 94510 ▁தெரி 94506 ാട 94504 ിയ് 94504 ํารวจ 94502 奏 94501 ▁Tier 94500 ▁hok 94500 詳細 94500 нії 94497 ित्व 94491 ưởng 94487 uslik 94483 ▁айт 94482 ▁đẩy 94477 ▁bate 94476 ▁შექმ 94473 ▁पाह 94471 ▁dheera 94469 াউ 94468 ▁makasi 94465 ▁sair 94463 ▁sağlık 94461 ▁carbon 94460 培养 94460 ▁גאר 94459 ▁Daf 94458 各地 94456 लेली 94454 ▁legisl 94452 βη 94448 ▁온라인 94448 asining 94447 edem 94447 ementet 94447 ▁ಬಾರಿ 94447 即使 94447 ▁mne 94445 ▁deene 94444 自分が 94444 ▁ազգային 94441 嘴 94440 ▁Kitab 94439 eteve 94437 ▁Airbags 94436 歲 94436 мь 94435 ▁evident 94435 ▁16:00 94432 ▁Motiv 94432 ▁статус 94432 ೇಟ್ 94430 ▁ude 94429 ▁Moj 94424 ▁୮ 94424 woord 94421 ېښ 94421 ▁jeunes 94421 rijed 94420 adilan 94419 ▁Acces 94419 ▁Eltern 94418 ▁birnin 94416 ▁самі 94414 ..!! 94413 ปั 94412 ლში 94412 ▁තත් 94412 ▁sejumlah 94411 ▁frum 94409 ▁início 94409 тивен 94407 ▁raskt 94406 ensis 94403 ındaki 94403 ▁başlam 94402 悲 94400 ुवा 94399 ▁مند 94398 ▁UL 94396 ાઈ 94388 ფერი 94388 agga 94384 ▁aurk 94384 ▁тээ 94384 šos 94383 業務 94382 ▁distintas 94381 olaeth 94378 xda 94378 лаго 94375 ▁راهن 94371 ΠΑ 94368 ▁duwe 94368 ▁причини 94368 otojų 94364 ▁Sexy 94360 ▁familias 94360 ▁αλ 94359 ▁tarvit 94351 न्छन् 94350 晴 94350 จิ 94348 ▁компетен 94348 rys 94347 ▁Perdana 94346 ▁lám 94341 ▁selesai 94341 ▁ખૂબ 94341 ▁ਕਾਲ 94339 ▁parfa 94338 فاء 94337 ▁त्याला 94337 мая 94336 เรือ 94336 ▁leor 94336 ▁modeller 94336 ▁ರಾಷ್ಟ 94336 ruan 94333 AF 94332 วะ 94331 რაფ 94330 ▁semi 94329 oxid 94325 விய 94322 stes 94321 ▁төҙ 94318 ▁دفع 94318 ▁meb 94316 ▁grein 94315 ▁տարին 94315 هدف 94313 тину 94312 ▁סק 94311 дент 94310 ▁terapi 94310 ▁заман 94306 czes 94304 ▁hent 94304 ▁Florida 94301 റോ 94300 ▁elemento 94299 契約 94297 ▁tipus 94296 arbete 94294 ▁vras 94292 ▁məhsul 94291 ຮງ 94289 erana 94287 ▁gyakor 94286 ▁giám 94285 ▁اتي 94285 ▁<< 94283 ▁“ 94282 ▁сним 94282 ▁بحران 94281 oksia 94276 atk 94275 ▁BAL 94275 ▁предупре 94275 ▁fútbol 94274 ▁аға 94273 ÍT 94271 돌 94271 ഷ്ട 94266 ▁baé 94264 ▁پڙه 94263 dymo 94262 ▁ഹോ 94260 ૦૦ 94258 வன 94256 ከር 94256 ▁šeit 94256 ▁Noi 94255 ▁هيء 94255 加上 94252 นะครับ 94250 ▁Avec 94250 ▁මුදල් 94245 ▁Laat 94240 ▁utvr 94237 andes 94235 neb 94235 ▁LikaNkulunkulu 94232 ilka 94230 שימה 94230 ▁protección 94230 ▁Pack 94229 ▁ծրագիր 94228 ▁veľké 94224 ▁કુ 94223 ဉ် 94222 ▁zesummen 94221 पालिका 94219 ธานี 94219 ລີ 94219 ▁لڳي 94217 μορφ 94216 ଗତ 94216 ▁maximum 94216 mụ 94214 ▁werkt 94213 වුරු 94211 ▁Inte 94209 ँड 94206 去了 94206 urik 94203 вли 94200 ▁портал 94200 ▁preh 94199 ▁forse 94197 ▁especie 94194 ▁бүхий 94194 ▁različnih 94192 ▁целом 94192 шага 94190 ▁כול 94187 okwazi 94186 ေလာက္ 94186 瓦 94186 ▁ҡалаһында 94185 νι 94184 ▁мати 94184 ▁Tamén 94183 ▁ҡатнашы 94181 ଵ 94180 ▁Diyos 94179 ▁පසුව 94177 czą 94176 ▁საქმე 94176 ▁kaynak 94171 emuan 94169 ▁Meny 94167 ▁نقصان 94166 ▁الإثنين 94164 鲁 94164 ▁življenje 94162 ▁ард 94162 astas 94161 ▁niets 94159 ារី 94158 ▁լուծ 94155 ▁közöss 94153 ▁tullut 94153 ҙаҡ 94151 ദാ 94148 ▁Prze 94147 ▁luogo 94146 жні 94145 ▁шоу 94142 ոսի 94138 ▁Вя 94138 ùa 94136 ස්ත 94136 ▁napl 94136 şdır 94135 ūp 94134 сади 94133 слава 94129 ekileyo 94127 ▁După 94126 ▁Kund 94125 ▁Hakim 94120 ▁larawan 94119 ▁hál 94118 ▁Questa 94117 ▁performance 94116 ▁toti 94116 зването 94114 ▁Odisha 94114 いつも 94114 Den 94113 ▁ترم 94109 ildir 94108 पाद 94107 ▁negocio 94106 ▁sagala 94103 kaus 94102 ▁minus 94101 ▁forex 94100 ▁femmes 94098 ▁ndii 94098 ▁түрлі 94098 ▁ராஜ 94093 gum 94090 ▁začet 94089 ït 94087 ▁киле 94086 ▁컨 94086 ▁Hasil 94084 ▁SIA 94081 ▁ရွိ 94078 ajul 94076 attal 94074 ▁دکھ 94074 ▁Cil 94071 ▁kete 94071 low 94070 more 94070 ▁impi 94069 ▁статьи 94069 就能 94069 ▁taifa 94067 ▁former 94066 ▁Закона 94066 ▁használat 94065 ▁tone 94065 Ва 94062 体验 94062 ▁لبنان 94060 ▁이벤트 94060 eel 94058 ▁Hamburg 94058 ▁guvern 94057 ▁aith 94054 onso 94053 alarını 94052 ğü 94050 ▁beharko 94050 ▁vaca 94048 ▁येथील 94047 ▁свого 94045 ▁ademais 94044 ▁Gha 94043 ▁خواست 94042 還有 94042 ▁2002, 94041 ▁perkembangan 94039 ▁ත් 94032 ▁fakta 94029 ▁степени 94029 tavia 94028 ▁zdroj 94028 ไทยรัฐ 94027 每年 94027 ▁DAL 94025 ▁Mk 94025 ▁silloin 94024 ▁сколько 94023 ▁sữa 94022 WI 94021 ▁operator 94021 ▁තමන් 94020 ▁przedsiębior 94019 davo 94017 ▁ühend 94017 ▁جمال 94017 ▁არიან 94015 middag 94013 ▁ξέ 94013 这次 94012 ▁ପୁଣି 94010 ኘ 94009 ▁կա 94009 ▁kształ 94005 ▁Bitte 94004 ▁неде 93999 ▁ყველაფერი 93994 логу 93993 ಎಂ 93992 ▁edebil 93992 ▁Award 93991 ▁mogoče 93989 ▁raad 93989 isió 93988 ▁հիմնական 93988 omus 93987 ▁Student 93986 angaza 93984 ARAN 93983 έα 93982 ▁momentul 93982 ▁samalla 93982 ittu 93980 ▁ਵਰਤ 93980 ▁पड़ 93979 ▁conex 93978 ▁rhaid 93976 ▁провод 93975 ▁фестива 93975 มนุษย์ 93974 bụ 93973 ▁κόσ 93964 ▁३० 93962 ojik 93958 ▁آش 93955 ▁اسک 93953 ijken 93951 ▁Graf 93951 ▁tří 93951 ▁কিন্তু 93951 ۃ 93950 ବାଦ 93949 ਗੂ 93948 ▁Xuân 93947 ▁onwe 93947 ▁געד 93946 競 93945 ▁naša 93944 ▁ראשון 93942 ▁riconos 93941 ▁풍 93938 ENTE 93937 ▁Gdy 93932 ▁podataka 93932 ▁არას 93932 ▁искус 93931 ▁съд 93931 らの 93931 ñan 93930 ▁Fashion 93929 НУ 93928 كلة 93920 ▁prépar 93918 ▁ਭਾਰਤੀ 93917 ▁rappresent 93916 ▁veten 93909 gue 93908 ▁ຝ 93907 ▁საშუალ 93906 ▁feadh 93905 ▁irrit 93903 ▁mňa 93899 ratas 93897 অ 93896 endő 93895 ▁लिख 93894 alean 93893 tiiv 93893 ▁Erit 93892 ▁eğ 93892 3/ 93891 წვე 93888 amı 93883 練 93882 अघि 93878 ▁ainm 93875 ▁поги 93875 следова 93873 মন্ত্রী 93873 ▁montag 93872 ▁عاشق 93872 ▁meisten 93871 ▁ohje 93869 ▁тау 93868 ▁хүлээ 93868 ▁Dž 93867 ▁Itt 93867 ěli 93866 ▁socials 93864 邀 93864 ▁วัด 93863 オー 93863 ▁Azo 93861 ▁виробни 93856 푸 93856 ▁canta 93855 ▁Golaha 93850 ▁dosa 93850 尾 93847 ▁chờ 93846 ▁meeld 93846 тором 93842 ▁berasal 93841 ▁நீதி 93841 ▁Consili 93839 ▁iddi 93839 ▁спеціаль 93837 ▁хлоп 93837 ິ່ນ 93835 unjalo 93832 δω 93832 भिडियो 93831 ▁ارشد 93831 ▁naziv 93830 ▁οποίος 93829 ▁bayar 93828 ▁scri 93826 ▁Kono 93824 ▁келеді 93824 stig 93823 ▁куда 93823 lih 93822 ▁představ 93822 ▁ਤੁਹਾਨੂੰ 93822 ▁узнать 93820 enguèt 93818 éli 93816 െന്നാണ് 93814 ▁poł 93814 ▁किसान 93803 ▁brukt 93801 ▁Бя 93801 ▁પાણી 93801 ▁krās 93799 ▁পূর্ব 93799 गाव 93798 ▁Sea 93798 ioje 93796 **** 93793 ▁компаний 93792 ▁മലയാളം 93792 ▁لڑ 93788 nent 93787 чително 93785 zystać 93784 ١ 93782 ▁Anu 93779 liyinin 93773 ▁chegou 93772 ▁harren 93771 ▁ಹೇಗೆ 93770 ▁kulture 93769 ▁иргэд 93767 인데 93767 얼 93766 λωση 93763 ▁هستید 93763 ▁hír 93762 ▁aquestes 93761 ▁жүргізу 93761 ▁باقی 93760 ▁এম 93760 anm 93759 ▁betyr 93758 してしま 93758 gruppe 93757 ύτε 93757 ▁ਮੈਨੂੰ 93754 ▁ਭਾਈ 93750 ▁titre 93749 ▁tyle 93748 ▁එන්න 93748 ▁ବିଜେଡି 93746 デザイン 93745 ▁scar 93744 ▁kiri 93739 uksiin 93737 全て 93735 ▁հիմն 93734 สัก 93733 ▁AND 93732 வச 93728 ▁príspev 93726 ▁üzerinden 93725 roduc 93723 ুয়ারি 93723 ▁رپورٹ 93723 ▁boyunca 93722 ▁destacar 93722 ▁প্রতিষ্ঠ 93722 ▁kaffe 93719 ▁berlangsung 93718 ▁эко 93718 新しい 93718 ▁MET 93715 ▁здраве 93715 ▁категории 93714 utsi 93713 ▁обслу 93713 ▁systému 93708 ләт 93707 충 93707 ▁voorzien 93701 ▁লাই 93699 ▁Colombia 93698 rati 93697 ▁επέ 93695 бити 93693 والي 93690 ោយ 93686 ▁jeune 93686 ▁ambul 93685 ▁ekipa 93684 ▁moat 93680 ▁ηλεκτρον 93680 ▁Febroary 93675 ▁Мон 93673 ▁Москов 93666 ▁pasak 93660 ▁خاک 93656 ▁túto 93653 즘 93653 elkie 93651 ▁வெளி 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93421 กระท 93418 ▁lyder 93416 ▁πραγματοποι 93414 реді 93411 ▁എന്നെ 93410 منٹ 93406 鱼 93406 ▁stej 93403 ▁практика 93402 ois 93396 жност 93396 ▁pornog 93395 ▁Hello 93394 ▁ආරක්ෂ 93394 ▁സത്യ 93391 ▁خپ 93390 ▁nayo 93389 änk 93388 ▁Sweden 93388 ▁بچه 93386 德国 93386 ▁постій 93385 ▁dingen 93384 ▁lich 93384 ▁ପାର 93380 ▁පොඩි 93379 ▁මතක 93379 ▁shule 93378 ▁Skup 93376 enzeke 93374 ัวร์ 93374 銷 93374 ▁häm 93373 ▁kait 93370 魚 93370 ▁דקות 93369 ▁აი 93369 ▁dårlig 93364 ▁Други 93364 ▁Comhairle 93357 ▁létre 93357 ▁πρωτο 93356 пит 93355 ▁liek 93354 ▁жат 93351 ▁kemb 93349 ▁ათ 93347 ▁디자인 93347 ▁ፕሬ 93346 សភា 93345 ▁valt 93345 ચના 93341 ▁раён 93341 eliu 93338 ▁британ 93337 itez 93336 的方式 93336 ▁formes 93335 ▁esetben 93334 ▁помощь 93334 ष्य 93332 িস্ত 93331 ▁offizi 93331 GT 93327 ▁չեմ 93327 ttä 93324 zlich 93318 ෙහි 93318 экономи 93314 ▁yani 93314 ▁Świ 93313 ▁השת 93310 ▁gawe 93309 IMO 93308 भे 93304 ▁preocupa 93299 ЭН 93295 ▁Kamer 93295 ▁asing 93295 ▁plutôt 93295 ▁tratamento 93292 ▁дій 93290 ▁decyz 93289 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93158 ▁සොයා 93155 ▁ofisi 93153 ahụ 93152 ທີ່ຈະ 93151 ▁عشر 93151 아가 93151 illy 93150 qit 93143 ▁ճանապարհ 93142 ▁Finans 93138 」「 93138 ▁موفق 93137 лети 93134 тельность 93134 թե 93133 ▁arbe 93133 ▁有 93133 ▁Kesehatan 93132 ▁шест 93132 jeće 93131 ▁annonser 93130 ▁proyekto 93130 ▁deegaanka 93129 ▁Бөгөн 93129 andraisana 93128 ▁bukti 93128 гел 93126 eit 93124 ivar 93122 ▁senare 93122 ▁ажиллах 93122 ettem 93120 ▁इससे 93118 ▁प्रत्य 93118 軟 93114 ▁այ 93113 μβα 93112 इड 93108 ▁zs 93108 ▁премия 93107 ▁оруула 93105 ▁divid 93104 ಿಸಿದೆ 93102 ▁Mű 93100 ▁бүрэн 93100 特殊 93100 ▁ಅಭಿ 93099 штине 93097 ▁기대 93097 niedz 93094 ▁patru 93092 ▁tillf 93087 кө 93085 दिन 93082 ায়ী 93082 angani 93080 เด็ด 93077 ▁ମୃତ୍ୟୁ 93077 циони 93076 ▁povedal 93075 ▁(* 93074 ▁виро 93074 ▁കിട 93073 සිංහ 93072 ▁bilet 93070 μοι 93069 ▁diev 93069 ▁kej 93069 mişdir 93067 ствата 93064 ողական 93064 گذ 93064 hatsi 93061 ávať 93061 nimo 93059 ▁Huy 93059 ▁٢ 93059 ▁የደ 93058 錄 93058 ▁рай 93057 ▁الأرض 93057 тоў 93056 ▁öffent 93056 ▁Iki 93055 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92200 ▁1924 92199 ▁საუკეთესო 92198 acă 92197 ▁εμφαν 92196 측 92196 में 92195 hlaw 92191 नाव 92191 ▁CU 92190 ▁partici 92188 ▁tiket 92188 ▁прия 92188 มั 92181 ▁pyt 92181 erner 92179 ▁idd 92179 ▁бергә 92178 ▁tradu 92172 ▁تز 92172 ▁застосування 92170 ▁дитя 92169 лази 92163 ▁informāciju 92163 ▁नेकपा 92160 ▁ā 92158 ▁جلو 92158 ტორი 92154 alyse 92152 ▁suomala 92152 imana 92148 ▁esos 92148 ▁चै 92148 ▁keret 92145 ▁probeer 92145 ▁circul 92144 ▁monet 92144 ▁roinnt 92143 ▁nies 92142 ત્મ 92141 ণের 92136 ▁bege 92134 ▁mwezi 92134 ោស 92132 ▁በተለይ 92128 ▁nna 92127 ▁zahr 92127 ▁בשי 92127 ติดต่อ 92125 ▁อยู่ 92124 шили 92122 責任 92122 ntu 92121 ▁אויפ 92121 နဲ့ 92120 ▁qarshi 92120 ▁demo 92117 ▁reserva 92116 ครบ 92114 pozna 92112 ▁हुँदै 92112 ▁Kementerian 92111 ้วน 92108 ▁operasyon 92108 ▁hamar 92107 ▁फी 92104 שרות 92096 ējumu 92094 ▁Poj 92094 rens 92092 とに 92092 ▁ایل 92091 ▁ተስፋ 92091 ▁imate 92090 ▁അംഗ 92090 ኣ 92088 ▁saanut 92087 ▁Ян 92087 ಂಬರ್ 92086 ▁cantar 92085 ▁toujou 92085 ▁денеска 92085 ▁գեր 92085 خار 92083 ▁äär 92083 անքի 92082 ▁Isp 92078 givning 92076 უსი 92075 mum 92074 ድሞ 92074 ▁аэ 92074 ஸ்ப 92073 ▁нутгийн 92073 ന്യ 92071 ▁успо 92069 plication 92065 سازی 92063 ▁dituzten 92060 दय 92059 ▁invers 92056 が見 92056 ▁македонски 92054 ljiva 92052 ▁Ofisi 92052 來的 92049 alität 92047 ▁Mlad 92046 ▁वाप 92046 cesa 92044 ▁IF 92042 ators 92037 other 92036 ▁38. 92033 ▁нито 92033 ΙΝ 92030 ţional 92029 ▁เบ 92029 ▁아무 92028 gach 92027 ▁emocion 92027 ▁tšeo 92027 ৷ 92026 arsch 92024 ▁مبا 92024 ▁tulad 92023 ಪುರ 92021 ▁قالب 92018 ▁pasu 92017 ▁beeld 92015 ▁kunn 92014 лектә 92012 ോധ 92010 ᄏᄏ 92010 ประกัน 92008 ▁prób 92007 ▁Hii 92006 黃 92004 ▁کشورهای 92003 lair 92002 evel 92001 ▁kaun 92001 ર્મ 91997 ▁Արտ 91997 ▁ukufunda 91995 дана 91994 үүр 91993 วัง 91993 ▁sabor 91992 ▁Kemmañ 91991 ূর 91989 alls 91988 ▁Ù 91988 ▁besed 91987 晚上 91987 ▁mínimo 91986 lumotlar 91985 oen 91985 onaa 91985 ഞ്ഞെടു 91981 ▁ફર 91980 ▁Kano 91978 ▁храни 91977 ▁décembre 91975 ▁ngữ 91975 inté 91971 ▁barre 91971 стання 91967 ▁wskaz 91965 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91725 ▁såg 91725 ▁метр 91724 მები 91721 සුන් 91719 ymai 91717 ▁Inici 91716 ▁fita 91714 ▁machine 91714 ▁ngaphakathi 91714 ▁dovolj 91710 ज्ञान 91708 지고 91706 телями 91705 hoù 91704 ▁bassit 91704 كمة 91701 പ്പെട്ടു 91701 ▁Guvern 91700 ▁sieci 91700 ▁ਨੌਜਵਾਨ 91700 ोबर 91699 ▁सक्छ 91698 ल्ट 91694 ▁Semb 91693 place 91690 zera 91690 isso 91689 ▁зва 91688 เคราะห์ 91684 ▁Color 91683 ▁kirit 91683 ▁ғү 91683 드는 91683 ▁pü 91681 ▁иҫәбе 91681 丈夫 91681 yó 91680 ▁Сергей 91678 ▁pest 91675 סטן 91674 ▁Obser 91673 רונה 91672 ▁Koment 91671 klärung 91670 ার্থ 91665 ▁confl 91663 曜 91662 ▁érint 91661 lauf 91660 ▁دقیقه 91660 ▁kusa 91659 ▁Kapital 91657 ▁pieņem 91653 כש 91651 łuż 91650 Der 91649 cate 91645 МУ 91645 ▁Jerusalem 91645 ▁سمیت 91642 gó 91641 られます 91640 fors 91637 ▁газо 91636 ustele 91635 ▁čty 91635 wenzeka 91634 ▁Dead 91634 ▁NC 91633 ▁avtor 91629 ▁najwy 91629 ▁vivaroaupenc 91627 ▁Olimp 91626 ▁Tev 91624 ▁pretende 91619 inais 91618 uración 91615 νώ 91614 ▁iqtisodiy 91613 ▁(22) 91611 строен 91610 ▁matar 91610 ▁miks 91610 ፦ 91607 ▁Jog 91604 κει 91602 Biz 91600 ▁proiektu 91600 ▁στά 91599 ēķ 91598 ▁ייִדיש 91598 ▁desenvolup 91597 ▁pér 91597 ▁telur 91594 ▁яко 91594 ▁сургууль 91593 ▁camping 91591 ▁nganti 91591 ▁있으 91591 ▁חס 91589 ▁settimana 91587 ▁ҡатнаша 91586 ▁Galiza 91582 ▁tere 91581 ▁הדי 91581 ▁ועל 91580 来看 91580 ▁vanh 91579 ▁մարդկանց 91579 ▁bukaNkulunkulu 91578 ▁duz 91578 ▁سرگر 91577 ējas 91574 ម្នាក់ 91572 ▁نمایید 91570 ▁immun 91569 ▁kinaminosang 91569 ▁lõp 91569 ▁modu 91569 ▁ತಾಜಾ 91569 ▁proizv 91568 ionych 91564 ▁feltételek 91564 ▁پښت 91564 禮 91563 ▁ദിവസ 91562 ▁Kamera 91560 اريخ 91559 มากกว่า 91559 ▁ଜେ 91558 antwort 91557 телгән 91557 ▁udele 91557 ▁ር 91557 UMA 91556 führung 91556 ▁nivå 91554 ▁langa 91553 unis 91552 ▁politiko 91551 intang 91548 ▁divizoù 91548 ▁lì 91547 ▁tekrar 91547 ▁너 91544 skri 91543 ▁худож 91542 kert 91540 ▁37. 91539 ▁nden 91534 කො 91533 ▁matk 91532 ▁فارس 91531 ▁wujud 91530 ▁produção 91528 ▁servicii 91528 ท่า 91527 ▁շարունակ 91527 群众 91525 šką 91524 лька 91524 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90382 ▁сацыя 90382 ▁Forsk 90381 ▁kres 90380 മായിരുന്നു 90379 ▁возника 90379 तन 90375 ្លែង 90375 JEN 90373 மார் 90373 ekelo 90372 ▁රාජ 90372 ▁proz 90370 ▁පැමිණ 90370 ▁मानिस 90369 gación 90366 ▁giriş 90366 ಗ್ರ 90364 ▁Sera 90359 ▁lepiej 90358 emark 90355 ▁ieri 90354 Mac 90353 ▁Klaus 90351 ▁kaniak 90351 ▁حلق 90351 ▁trova 90350 ابقة 90348 abari 90345 daşı 90342 ▁რატომ 90342 ▁सवाल 90340 ▁Всі 90337 ▁passado 90336 ▁buruk 90335 ▁acestei 90334 ▁jente 90334 ësore 90333 ▁komanda 90332 plijout 90331 ▁проку 90331 ২০১ 90329 ▁negocia 90328 ▁Isto 90327 ▁اعمال 90327 ▁lakhe 90326 ▁უკან 90326 icol 90321 ▁колко 90321 לח 90319 űt 90316 cun 90314 ▁acept 90314 ▁គណបក្ស 90314 ilmek 90312 ▁Justi 90311 ▁жін 90310 öff 90308 וקים 90308 ورم 90306 ിക്കാ 90306 ▁حقيقت 90301 ▁▪ 90300 ukung 90298 ▁etilgan 90298 ▁Made 90297 ▁ଅସ 90296 ▁Ainda 90295 ▁augst 90295 ▁тис 90295 štění 90293 ytis 90292 برا 90290 ▁Jej 90287 ▁зра 90287 ▁ጸ 90286 իոն 90284 øret 90282 ▁ngokuk 90281 ▁partid 90281 ିନି 90277 ტუ 90276 muigh 90274 oar 90274 status 90270 ▁ИН 90268 ▁ছাত্র 90268 ▁Christi 90263 dn 90262 angun 90261 총 90261 ▁нез 90260 ែរ 90257 燃 90257 ▁Amar 90256 uron 90254 žite 90252 ▁նկար 90252 ▁받아 90252 ▁lacag 90251 ▁proč 90251 ▁nieko 90250 ▁Mek 90248 ▁башлай 90248 ▁୧୯ 90243 ▁săptăm 90242 щее 90241 ▁booda 90241 ▁intermedi 90241 cup 90240 ▁њих 90239 ጢ 90238 ▁уға 90235 זו 90234 ▁ungu 90231 ▁Obr 90229 ▁жоспар 90228 ▁izku 90227 цар 90223 ▁hilang 90223 ▁Hola 90218 ▁traz 90218 ▁бизне 90215 agog 90213 qiqat 90213 altar 90212 тые 90210 manni 90207 ▁현대 90204 ▁wọ 90203 ▁નવી 90203 ▁pán 90201 ▁ҡур 90197 gak 90196 สําเร็จ 90195 ▁روند 90195 adian 90192 žna 90192 ▁ڪوشش 90192 졌다 90192 ▁navega 90184 ▁heims 90181 दर् 90179 ▁fullt 90179 ▁lavi 90179 ▁öss 90178 ▁ouv 90177 ▁পাল 90177 ούνται 90176 ▁kude 90175 illaan 90172 ▁152 90171 ▁булыуы 90171 ▁cakap 90164 ▁ذریعے 90164 ▁upravo 90163 ന്മാ 90161 ▁pitanje 90160 ▁puteti 90159 ▁Aho 90158 ▁diferite 90158 感情 90158 peria 90157 ▁mafi 90157 ▁Nagyon 90156 ▁mayores 90155 ▁päät 90154 ▁шығарма 90153 ▁Ukraine 90151 ▁Marcel 90150 ▁gagal 90147 ▁tantara 90142 ▁Alang 90141 ▁dejta 90141 癌 90141 ▁KK 90140 ▁Києві 90139 ▁येत 90139 ▁йәмғи 90137 之外 90137 เดีย 90133 ▁Familien 90130 ▁afl 90130 chol 90128 ▁слишком 90125 ▁კომენტ 90125 ēšanu 90124 ▁02. 90124 ▁ű 90124 的心 90124 ভাব 90121 ▁Saúde 90120 ▁inferior 90120 lado 90111 ▁unang 90110 ▁природа 90105 ▁ከሆነ 90105 مند 90104 ▁Шы 90104 ▁самый 90104 ሳቸው 90102 理想 90102 ▁vantag 90101 ▁entro 90100 ▁mle 90100 ▁Хята 90100 ▁observa 90099 sih 90098 ванням 90097 ▁monar 90097 ▁yiz 90097 ▁អំពី 90097 adenn 90095 ▁सम्झ 90093 ▁moontlik 90090 ichièr 90089 ظمة 90087 ▁παρουσιά 90087 ละเอียด 90085 ამა 90085 ெட் 90084 ▁menjelaskan 90084 цыйна 90082 ▁ഇരു 90081 ließ 90078 ▁مرگ 90078 ▁وتح 90078 ▁uile 90076 ▁ruo 90075 ▁Резу 90070 ğine 90067 ▁регистрации 90067 ▁גאנ 90066 ෙත් 90063 ຕີ 90062 ▁význam 90061 iicht 90059 éria 90058 ▁ნათ 90056 ▁দল 90055 ▁ठूलो 90054 iseid 90051 ьяҡ 90051 မၼ 90051 ▁ràdh 90051 ▁giro 90050 Internet 90049 ▁priemon 90047 嘉 90047 ▁duch 90045 vasti 90043 รายละเอียด 90041 ▁xener 90039 ▁faa 90037 ▁ilo 90037 ▁cineva 90036 ▁Beau 90035 ົ່ວ 90034 টির 90029 ▁имаме 90029 ရဲ့ 90028 ▁پئ 90028 手段 90028 тэн 90024 ▁139 90024 ▁burger 90020 vė 90018 ▁febrer 90018 ▁condiv 90016 ▁त्याच्या 90016 ▁produzione 90014 ▁рет 90014 ▁moksl 90013 ngor 90011 ▁odm 90010 ▁tersedia 90008 ▁blijft 90006 ▁Medien 90005 ▁Уча 90003 ▁فرانس 90003 ربي 90002 ຮ່ວມ 90002 ▁ମିଳ 89996 ▁paste 89995 ▁առան 89995 ვიდა 89994 ▁впер 89994 image 89992 רוק 89992 स्य 89991 ▁عربستان 89991 kleri 89988 ▁gând 89988 ▁ós 89988 ▁کردی 89988 ም፣ 89985 ▁чини 89985 ഹ് 89982 ▁proyectos 89982 及时 89982 chw 89981 ▁төҙөлөш 89979 ▁hjär 89978 τερα 89977 ূর্ণ 89977 ikita 89976 ▁Austria 89976 ▁Gros 89973 ▁procesos 89972 ▁ஏன் 89972 mıştır 89970 ▁потпу 89968 ▁pea 89967 yder 89965 ▁İzmir 89962 abta 89961 واره 89961 ▁நேர 89961 ▁өн 89959 ▁जुन 89957 ▁jetën 89956 ▁қоғам 89955 ссий 89954 ▁публі 89954 ▁கூட 89950 ▁Vieš 89947 ▁Олексан 89944 ав 89941 ▁μει 89940 ▁dout 89939 ungal 89937 ▁നിർ 89935 ▁νη 89934 ສະເ 89933 ▁ета 89933 ▁perchè 89932 感謝 89932 ▁onnist 89929 ▁ඔහුගේ 89928 ▁davan 89926 وعات 89925 ▁ภาพ 89925 ▁والي 89924 ▁dług 89921 ▁bant 89919 намі 89915 違う 89914 ▁konnt 89913 ▁නීති 89912 ោធ 89909 adla 89906 ▁жасау 89906 yksiä 89905 гл 89905 でしたが 89905 ьте 89904 ▁جذ 89904 ēģ 89903 μορ 89903 ර්ණ 89903 ▁prezzo 89903 ▁қолдану 89902 ▁આપણે 89902 cure 89896 ▁здій 89896 ▁קלי 89894 pụta 89892 ୁଆ 89891 ▁Giải 89891 ▁ਦੌਰਾਨ 89890 ▁வழங்க 89884 ▁1938 89883 ▁miliard 89882 ▁pengetahuan 89881 ▁որեւէ 89881 ▁ต่อ 89879 ▁uds 89877 ▁২০ 89877 ляє 89874 cripción 89869 седа 89868 옥 89868 ▁andare 89866 ▁mengandung 89865 ▁zwięks 89862 ▁odlič 89860 ▁श्र 89860 ▁ශි 89860 ▁MY 89859 ▁zmieni 89858 adil 89856 ▁continuare 89855 ▁trading 89853 再次 89852 łada 89851 ▁jedem 89851 ▁urg 89849 ρεία 89846 ▁səhif 89844 ▁wymag 89842 തല 89838 ▁rul 89838 ▁befol 89836 quot 89834 ▁verkar 89833 юу 89831 ▁тъ 89830 ដី 89827 ▁Cart 89825 ▁гады 89824 ▁درواز 89823 ▁τρί 89822 ▁පත්ව 89822 ▁सोच 89821 ▁кенә 89820 ▁πρόγραμμα 89819 ▁Kraj 89816 ▁archit 89815 ィ 89814 摘 89814 ▁potrivit 89813 ▁pendek 89811 Aqu 89810 ▁стави 89808 ▁Krit 89807 ▁száma 89805 ▁administ 89803 ▁teanga 89803 発表 89797 ▁həll 89790 ▁leath 89790 eeya 89783 ▁― 89782 ▁sürü 89778 ວຍ 89777 ▁प्रयत्न 89773 ▁тръ 89771 ▁reas 89770 ▁සමහර 89764 ▁úspe 89763 ▁שפּ 89763 ▁сце 89762 ▁утра 89760 ▁បច្ចេកវិទ្យា 89760 чыны 89756 owaniem 89753 ▁പോലും 89753 ənc 89752 eties 89751 ▁Spl 89750 ▁будуць 89750 ingkat 89749 ▁spro 89748 ձն 89745 राई 89745 ▁พิ 89744 ശ്ര 89741 суль 89740 ▁ေလ 89739 ньому 89738 ļus 89737 ▁fits 89734 redno 89733 ▁okwu 89733 ▁КП 89733 ジェ 89733 phe 89731 ▁коммента 89731 ▁Soomaalida 89729 ▁dinheiro 89729 alth 89726 entorn 89726 ▁ලිපි 89724 ▁busc 89723 ▁Chamb 89722 ▁չուն 89722 ▁fă 89719 ▁Festa 89715 ▁hirem 89713 ໃສ່ 89711 displaystyle 89710 − 89706 ▁Septiyembre 89706 ционер 89705 ▁našem 89705 ▁субъек 89704 ukumu 89703 例如 89703 ▁ກໍ່ 89700 ৃতি 89699 СУ 89698 ▁Isten 89698 ▁začne 89698 getti 89697 ▁አጋ 89695 奋 89692 itou 89691 ▁бұрын 89691 jmë 89690 बारे 89689 ▁pavar 89689 ▁univerz 89689 ▁первую 89688 Ich 89687 ▁วิธี 89687 meler 89685 ▁virksomheder 89685 ▁اشتراک 89685 ▁Namen 89684 ▁ulah 89684 ▁comprend 89683 ▁Ont 89681 했던 89681 ▁bueno 89680 ▁anum 89678 ▁برق 89678 ਖੀ 89674 iaľ 89673 ▁miljoen 89672 ▁ሁለ 89671 eun 89670 ▁noua 89670 ▁defa 89669 ▁akkurat 89668 ▁Giang 89666 ▁SUP 89665 ұлы 89664 ▁rendre 89664 obat 89663 тир 89660 laşı 89659 官方 89657 กรรมการ 89656 ▁Februari 89656 ▁room 89655 loed 89652 ▁ulici 89651 ▁веома 89650 ڍ 89648 ▁hjälpa 89647 ▁Һу 89646 ہور 89641 ▁apsa 89640 ▁Indonésia 89638 ▁jätt 89638 ЭГ 89636 вие 89636 ▁ಕೂ 89633 zii 89632 ▁Occitània 89632 лниот 89631 ▁usuario 89630 ācij 89629 ელა 89628 ▁GE 89628 ▁զարգացման 89627 पान 89625 ေျခ 89625 ▁Pis 89625 ▁toinen 89625 ▁Dienst 89623 ▁০১ 89622 ▁ರೂಪ 89621 tara 89620 ▁eigin 89620 ▁اخیر 89620 ▁atao 89617 ▁varten 89617 ▁fyra 89615 ▁ukubuza 89612 ▁Nepal 89605 ▁поиска 89604 ▁ქვე 89602 言う 89601 ▁wysok 89600 ▁اقبال 89599 ኗ 89598 راک 89595 न्ने 89594 agod 89593 프트 89593 QI 89592 ▁jemi 89590 ▁Marin 89589 ▁vrijed 89589 ▁اسرائیل 89589 ▁PAN 89586 wakhe 89585 ▁ഓരോ 89584 ▁exerc 89582 ▁түүний 89578 itudes 89577 ▁hend 89576 ▁attend 89573 ▁конф 89573 ▁գաղ 89571 ▁հայկական 89571 kina 89570 tl 89569 ▁dolara 89567 ▁συμμε 89567 ▁השנה 89561 ▁Roinn 89559 ặng 89556 verb 89555 ANY 89553 ostaa 89553 ▁ū 89553 এম 89552 ▁zajedni 89548 ▁ໃຊ້ 89548 ▁hestoria 89547 ▁cong 89545 ▁Babe 89542 ریان 89540 тация 89539 untfu 89538 ▁Tata 89532 ▁הכנס 89532 ▁автомобиль 89531 ▁оча 89531 oban 89529 ▁เน 89529 ▁Pati 89524 ▁گهڻو 89521 ▁පිහි 89520 abilecek 89519 ▁Тој 89517 ▁Buc 89515 ▁نظری 89515 chisi 89514 joer 89514 лик 89513 uaño 89511 ▁pakaian 89511 ▁fagu 89506 style 89505 verse 89505 лід 89503 max 89502 inken 89501 ▁ظلم 89499 ▁WEB 89494 ▁ಮೀ 89494 ▁ideia 89493 kör 89490 ▁ಜೀವನ 89490 ສອງ 89489 ▁xullo 89489 ▁næsten 89488 ▁ûnd 89487 سې 89486 ▁செய் 89486 ▁keresztül 89485 ▁baš 89483 ▁duomen 89483 ▁occitanas 89483 ▁ಸಿನಿಮಾ 89481 通り 89478 ▁تجارت 89477 ▁braz 89476 ▁chtě 89475 ▁өкі 89472 ▁العرب 89471 aryti 89469 ▁Qan 89469 wangan 89467 ▁priest 89467 શિ 89466 ▁kemenangan 89466 ▁बल 89465 ՆԵՐ 89464 yczna 89463 ▁švent 89462 双方 89462 ▁BAB 89459 akel 89457 ଡ୍ 89457 ▁21.0 89457 ulio 89455 ította 89454 ▁វិញ 89454 דישע 89453 лігін 89451 ливи 89450 ytter 89449 imais 89447 ▁literat 89444 زمان 89441 ▁روپے 89440 ▁neviem 89437 ▁problémy 89436 ▁päeva 89436 icznej 89426 ▁архитек 89426 ▁կոչ 89426 ▁принцип 89425 ░ 89425 τικοί 89424 ,500 89421 ▁سائين 89421 ▁dhënë 89420 ▁علاوہ 89419 腹 89419 ▁police 89417 äänn 89414 άζεται 89414 ▁ብለው 89411 画面 89406 ங்கம் 89403 ходно 89400 ▁täv 89400 ▁แก 89400 ーズ 89400 ▁deney 89398 ▁lạ 89398 ulur 89397 ▁creat 89394 mento 89393 実際 89391 uvu 89390 ँग 89387 ပေါ 89387 ▁Umuntu 89387 ▁Konkur 89386 ▁bale 89386 ▁conne 89385 ▁१७ 89383 ▁save 89381 ผู 89380 ▁verantwoord 89380 ▁створення 89378 相關 89378 ▁antwoord 89377 ▁amici 89375 を実 89375 ▁habil 89373 ▁thúc 89372 enskap 89371 ▁pulo 89370 絕 89370 ləm 89369 ▁liền 89369 ńst 89368 ▁नजर 89365 gaard 89364 ▁дослі 89364 ▁السابق 89364 ഷി 89363 new 89362 ətli 89362 ставки 89361 ▁várias 89357 ▁lant 89355 ▁octombrie 89355 erire 89354 skur 89354 ▁комуника 89353 ▁преступ 89353 唯一 89353 ▁Този 89350 ▁ज्ञान 89349 ▁sonque 89344 ▁затоа 89344 unna 89341 ▁ቁጥር 89339 ▁להפ 89337 urch 89332 යෙක් 89332 もので 89332 ಳ್ಳಿ 89331 ▁qüvv 89331 ▁Meter 89330 נן 89328 ▁Noma 89327 ▁punti 89326 ▁aktive 89322 ଡ଼ିଆ 89321 ▁quarta 89321 ▁podrá 89320 ▁թույլ 89319 ▁problemes 89317 dil 89316 たく 89316 ùi 89314 ▁суду 89314 ▁hjelpe 89313 ологи 89312 ▁bhliain 89305 ▁citi 89305 ▁تاہم 89305 ▁لق 89305 ▁terce 89301 ဆီ 89296 ▁Saor 89296 ▁જોઈએ 89296 ▁सुर 89294 στεί 89293 ▁kump 89293 MT 89289 落ち 89288 ojot 89287 ▁стой 89287 egg 89286 違い 89286 អង្គ 89284 ▁Merkezi 89284 ▁מערכת 89278 ▁04. 89277 ▁입니다 89277 ავალ 89276 ▁Dz 89276 אַפּ 89275 ▁främ 89274 ▁עיצוב 89274 ▁månader 89272 ▁Volkswagen 89271 ▁autour 89269 ▁Народ 89269 ▁tenaga 89268 ▁gorau 89267 јава 89266 きて 89262 ▁grill 89259 ▁ਸੁਰ 89258 ▁이러한 89258 ▁ತಂಡ 89257 ▁wystar 89256 ized 89252 ▁الدكتور 89250 ▁ingat 89249 ▁Mikä 89248 ▁fungerer 89247 თის 89246 ▁своем 89246 ারের 89245 ▁अन्त 89245 ▁manager 89244 ▁grade 89243 ▁Неза 89242 ▁үеийн 89240 ▁slutt 89239 ▁двох 89239 ▁Much 89238 ▁kawas 89236 ▁ний 89236 apur 89235 ▁2003, 89235 ▁خلک 89235 費用 89233 ▁පෝ 89232 ובע 89231 ▁улуч 89230 ▁formula 89226 ▁тяже 89223 wają 89222 ▁вкус 89222 ▁составля 89222 ▁पहली 89221 ultan 89220 方面的 89220 ▁થયો 89211 ▁yilapho 89210 ▁musika 89207 ▁pitanja 89207 ezie 89206 চ্ছ 89205 ▁1925 89203 ▁ឈ្មោះ 89203 rici 89201 じゃ 89201 த்தான் 89196 ▁ಮುಖ್ಯ 89195 ▁wenza 89192 lista 89191 ▁Reporter 89190 ▁kashe 89190 ▁פני 89189 ▁semuanya 89187 ▁serb 89187 ▁مشروع 89187 ▁Sez 89183 ▁тамо 89182 ▁آصف 89182 ▁إلي 89181 uting 89178 vec 89178 ▁желание 89176 ▁azzal 89174 ▁doonaan 89174 ที่ไม่ 89173 ▁itin 89173 ▁tấn 89173 ▁נאָך 89170 ▁عوامل 89167 писание 89166 արժ 89165 ▁Ավ 89165 ▁વ્યક્તિ 89161 ▁wako 89160 ijom 89159 siyalar 89159 ▁διάρκεια 89159 ▁Richtung 89158 ▁μπα 89157 нисти 89154 ▁фонд 89154 řen 89153 ▁Lisäksi 89152 нх 89151 қор 89150 urso 89148 λαι 89147 ▁Raf 89147 (' 89146 ▁Yle 89145 ▁pošt 89145 ▁anual 89144 ▁publiko 89143 ▁рата 89142 ▁педагоги 89141 ▁روحانی 89141 ▁дальней 89130 чкој 89129 ▁Конечно 89129 нику 89127 ▁antre 89127 ▁апе 89127 ▁labour 89126 ▁toest 89126 ▁atras 89125 reff 89123 庭 89122 ▁तेल 89119 ▁बिना 89116 ▁Apabila 89112 ▁ощу 89111 ತ್ತಿ 89109 siyon 89107 ▁Niin 89106 ambil 89105 òng 89105 ▁غذایی 89103 ▁बेह 89099 ▁ಆರಂಭ 89099 工作的 89099 σιά 89098 ▁čin 89098 ▁anez 89097 ▁Klima 89096 легенә 89094 ▁ensino 89094 ▁содржи 89093 стъ 89092 ▁Moja 89091 ▁પ્રતિ 89091 เจน 89089 ▁평가 89088 ▁ماہ 89087 ▁Этот 89086 Cre 89084 dió 89083 Windows 89082 ▁talán 89081 ▁elde 89080 ▁nieco 89080 ▁oppure 89079 ▁разлика 89079 бла 89077 ▁началото 89077 маа 89076 ▁besluit 89074 ▁αριθ 89073 ▁കാരണം 89073 ▁TAK 89071 ▁rech 89069 ង្ក 89057 സു 89054 linie 89053 дко 89053 ▁dėmes 89052 ▁বিশেষ 89051 ▁uthole 89049 ▁bakt 89046 ▁darf 89046 ▁ურთიერთ 89044 植 89043 рец 89038 ვები 89038 ▁mandi 89038 ▁театр 89036 ▁inspira 89035 ▁perust 89034 ▁인간 89034 мое 89033 ▁tisíc 89032 คุ้ม 89031 ▁оку 89029 ▁могуць 89028 ▁Mati 89020 edingungen 89018 ▁Must 89018 ▁Kubonakala 89013 ДО 89011 经常 89010 masyon 89007 ▁Chicago 89007 ▁έπ 89007 ▁društ 89006 ▁Vau 89001 ▁gjet 89001 ▁yıld 89001 ▁втра 89001 ▁hài 89000 isesta 88998 ▁Beli 88997 σκε 88995 ixh 88994 ▁ಆರ್ 88992 inlik 88990 ହିତ 88989 ▁dhaab 88988 ▁grie 88988 ▁adopt 88987 ▁ಕೆಲ 88982 ңге 88981 ▁erbyn 88981 ▁vâr 88981 ▁terd 88980 ▁ответствен 88980 ▁претста 88980 immä 88978 fut 88977 ▁σημαν 88976 ▁bibli 88971 倫 88970 ▁прона 88969 රාධ 88967 yı 88966 ▁Betrieb 88965 ▁инә 88965 agg 88961 บัตร 88959 ▁lukaNkulunkulu 88959 ▁שבוע 88959 შობ 88957 ▁siyas 88956 ▁ਦਸ 88953 ໍ້າ 88951 ▁RH 88950 としても 88950 valla 88949 ▁maalin 88949 ▁ଉତ୍ 88944 ▁อีก 88943 ▁هنگام 88939 ▁ਖੇ 88939 ▁відноси 88937 abt 88934 골 88934 ▁swan 88933 ▁belangrij 88931 裏 88931 ▁1.6 88925 ▁emprego 88925 onica 88924 aaa 88923 ▁лин 88923 ▁жақ 88920 ▁vient 88919 ▁இணை 88919 ərdə 88914 ตั้งแต่ 88913 ▁රපට 88913 huma 88911 вшие 88911 ▁práctica 88911 ▁adel 88910 ▁Doktor 88907 ▁Mõ 88907 信用 88905 ▁चिक 88904 ▁Și 88902 ▁sinón 88901 łego 88899 ólic 88897 ությունում 88897 świad 88895 žené 88895 ėjus 88894 ໄພ 88891 ▁ikh 88889 ▁жөніндегі 88889 ▁inz 88888 ▁таң 88888 ranga 88885 ຕ່າງໆ 88885 zuki 88883 ▁અભ 88879 ▁Elena 88875 ಛ 88874 ▁boz 88873 ▁bhar 88870 ▁χρό 88870 ▁мом 88870 ▁Occitan 88869 වාස 88868 ▁Ukuba 88867 ▁biashara 88866 一步 88866 國際 88862 ▁PET 88860 ▁الجديدة 88859 ▁中国 88856 ▁appart 88855 արմ 88852 ▁Pej 88851 eztet 88850 ▁daa 88850 ▁ماند 88849 ▁feladat 88848 ▁الآخر 88848 18. 88846 ▁minuter 88845 ▁päätt 88845 impin 88840 ▁ushq 88840 הי 88839 ▁įsta 88839 買い 88839 ▁restor 88838 ▁principale 88837 ▁vler 88835 რებული 88834 рыі 88832 ▁région 88829 ▁tood 88829 сію 88826 laka 88823 ▁bugün 88822 rapp 88821 ▁engelsk 88821 ▁folytat 88820 ▁уровне 88819 ▁αγορά 88817 ▁nature 88814 ▁zre 88811 jno 88809 љење 88809 ▁පන් 88809 ▁עש 88807 ▁Maamulka 88803 ፈልጋ 88800 執 88799 міністра 88798 암 88796 ▁uzav 88789 ▁기준 88783 чника 88774 ▁kredi 88768 igų 88767 ▁evalu 88764 ▁смарт 88764 äte 88763 ...( 88762 ▁Tanah 88758 ▁definit 88758 ▁روزنامه 88758 ▁ವಿಧಾನ 88758 რავ 88757 ▁ಸಾಲ 88757 ▁kecap 88756 ▁تقریبا 88755 riw 88754 ▁fanmi 88754 ▁samaz 88752 ҙарын 88750 ▁зарим 88750 ▁නොවේ 88749 czeń 88748 พนักงาน 88748 ▁functie 88745 ▁dáng 88742 ▁hızlı 88741 руют 88740 ▁leden 88740 ▁الثق 88740 плата 88739 ▁Millionen 88737 ▁початку 88736 ským 88735 ovación 88732 revet 88732 ▁realitat 88731 ▁הטי 88731 ավորման 88730 okh 88728 ▁aztán 88727 ▁αφή 88727 áték 88726 ▁bring 88726 ▁мара 88726 劃 88725 ▁Thuirt 88724 ▁timmar 88722 owę 88716 наҡ 88716 ▁mbrojt 88714 isjon 88711 ▁Director 88711 ▁konver 88711 գի 88710 دود 88710 ▁เวลา 88710 ▁akademik 88709 ▁verho 88709 ▁وروست 88709 ▁Kinn 88708 ANE 88707 ▁mnoho 88707 ▁Mə 88706 ▁paran 88705 ▁PAL 88700 住宅 88698 ہیں 88697 经历 88697 ▁Програм 88695 ួច 88693 ▁junh 88693 ▁uummata 88692 ▁motema 88691 uhnya 88687 ▁avion 88687 졌 88687 ▁តើ 88684 त्मक 88683 ቃት 88682 ▁pintu 88682 춘 88680 ▁Lö 88677 ▁הישרא 88677 ▁pertanyaan 88672 ਮਾਰ 88671 ▁акт 88671 ▁어느 88671 ▁Oneindia 88670 ▁lämp 88669 ▁reģistr 88668 ▁Сре 88667 豊 88667 ▁sadr 88665 tigkeit 88664 ▁frumos 88664 ▁ezifana 88663 ▁löyd 88663 ดวง 88657 ▁δηλαδή 88657 แพ 88653 名的 88652 ▁Osiyo 88650 ▁zači 88648 ▁داشتن 88648 合い 88648 ▁kungakanani 88646 ▁Hv 88645 ▁εξα 88645 ▁сою 88645 ▁նկատմամբ 88643 ▁مخاط 88641 ▁مدير 88641 ▁cilvēki 88640 ჩევ 88637 ▁בתח 88637 ▁gezi 88636 ▁obten 88636 ว่าเป็น 88633 ▁ଭଲ 88632 ▁Mí 88631 ▁mabilis 88631 шмә 88630 ▁veřej 88629 ▁자유 88629 ▁Viss 88628 Del 88627 õr 88627 ▁Hair 88626 LD 88625 odišn 88623 тағы 88623 ▁juin 88623 公开 88623 ächst 88621 ▁machin 88619 ጮ 88618 ▁experiência 88614 ກາງ 88613 徐 88612 тила 88610 ▁aelod 88610 わない 88607 љно 88606 ▁nakita 88603 ayam 88602 ▁Damit 88602 ▁меся 88602 ▁Fitness 88600 ▁hosszú 88599 undur 88598 ▁kalba 88598 inv 88593 ▁duhov 88593 珠 88592 æðum 88591 ▁يمكنك 88590 献 88586 ▁Entertainment 88582 ▁sẵn 88581 лда 88580 ▁Udinga 88579 ▁جنا 88579 贴 88579 ▁صاف 88577 ▁Πά 88576 ▁kely 88574 ▁Jūsu 88572 ▁corps 88570 ▁dovolen 88570 ▁Normal 88569 ▁увек 88567 ▁ավել 88566 ▁técnico 88565 ▁Farmaajo 88563 ▁вули 88563 quilib 88562 ▁vendin 88560 ▁faol 88557 ▁opere 88557 ṣẹ 88555 ▁ọjọ 88555 roe 88553 ▁Hvað 88552 ▁cyd 88552 ี่ยง 88551 ▁ryhm 88550 ത്തും 88549 ▁જાહે 88549 ▁LS 88547 ▁Splet 88546 тны 88543 ▁sebesar 88542 akholwa 88541 ▁uitgeb 88541 ▁あ 88540 nota 88539 ▁قوت 88539 Aus 88535 ónak 88531 ▁Phát 88529 клады 88527 ▁Što 88526 vissa 88525 ▁besteht 88524 ▁wakh 88523 ojums 88521 囲 88521 element 88520 ▁זעל 88520 ▁comen 88518 ▁Кру 88517 プレ 88517 邮 88517 ▁canto 88513 是一种 88512 ўня 88511 ▁Eilean 88508 ▁Magazine 88508 ▁Beauty 88506 ▁tady 88506 ▁ets 88504 ▁wase 88503 ▁señal 88502 ományos 88500 ▁Tig 88500 ▁oktobra 88500 ▁sady 88499 oux 88497 ▁129 88497 iselle 88495 ▁agreg 88495 ▁federal 88495 ೇವೆ 88494 ▁Brun 88492 ▁ספ 88491 daság 88488 ▁διαφορε 88488 ▁нака 88488 ▁Frem 88487 ▁cinayət 88486 ▁hasa 88486 ▁tasar 88485 порт 88483 ▁beha 88482 ▁ọhụrụ 88480 ▁hino 88479 ▁त्रि 88479 ▁виду 88475 asab 88474 ▁poten 88474 ▁үш 88473 berdin 88471 ▁mahasiswa 88469 ulması 88468 ▁зээ 88466 ▁Jane 88464 ésére 88463 qaan 88462 úch 88462 ტრი 88462 ▁membutuhkan 88460 ▁ድም 88459 ▁nemok 88458 ▁سخ 88455 ▁Delta 88452 ▁χο 88452 ▁réir 88451 ▁सांसद 88450 飯 88449 San 88448 ▁Grazie 88443 美味 88440 ෂණ 88436 扱 88436 ▁Ас 88431 ▁затвер 88431 ▁пожа 88431 ▁Labarai 88430 ▁έχω 88430 ▁близо 88430 ▁zorgen 88428 ▁Кога 88426 ▁አስፈ 88426 ▁ક્ર 88425 ▁kvalite 88420 ▁corazón 88419 ▁Bệnh 88417 ▁어려 88415 ▁foss 88414 ▁seveda 88413 ▁осве 88413 кторы 88412 ▁کردار 88411 ▁ສຸດ 88409 ▁betale 88408 ▁коруп 88408 ▁नोटिफिक 88408 форм 88405 ▁голови 88403 ▁kayu 88402 ▁успех 88402 ▁democ 88400 ▁parlam 88400 ▁බෝ 88400 неді 88399 ▁קשה 88399 ▁культури 88397 ayaat 88395 ▁رسد 88395 ▁காண 88395 효 88394 ▁ಸೈ 88393 ником 88392 ▁Uf 88389 ▁Систе 88388 ▁gihatag 88387 ▁স্ক 88383 श्य 88382 ▁شرا 88382 ▁Belediyesi 88381 amatan 88379 ▁latihan 88378 ▁äm 88377 ▁Oder 88376 ▁modeli 88376 ▁լեզ 88373 ▁ア 88373 ättä 88372 ▁pride 88372 ▁ketiga 88369 hív 88368 ▁qildi 88367 ंश 88364 ▁lleol 88364 ▁ഇത 88362 ▁laus 88359 ▁varför 88357 ▁ล้าน 88354 ▁ექს 88351 ▁Teraz 88349 iroq 88346 باح 88346 ▁Δε 88346 ▁dobäi 88342 ▁trao 88341 ▁xestión 88341 ▁malu 88339 ▁desch 88338 ▁جاتے 88338 ▁pangg 88337 -2020 88335 ▁ਵੱਖ 88334 igid 88333 ▁фабри 88333 ▁266 88329 ▁வாங்க 88328 atil 88327 ライン 88325 ▁လွ 88324 ▁ọnụ 88324 ▁indonesia 88322 ▁vrste 88322 ▁cấu 88320 .* 88315 ▁hoppas 88315 ▁skap 88311 ▁vef 88310 ▁jonk 88308 ▁õigus 88308 ▁உறு 88308 ובים 88307 ▁páx 88307 ▁эйә 88306 ▁կուս 88306 ▁Está 88305 ▁опште 88304 ືບ 88303 ၿဖ 88303 arnya 88302 ▁þr 88299 ▁Asturiana 88295 orsan 88292 ▁прыня 88289 מנות 88288 ຕ້ 88288 σχ 88286 නට 88281 ▁sederhana 88281 ▁configur 88277 ▁цвет 88274 ▁القرآن 88273 ▁அனை 88273 ▁Hvorfor 88269 ▁herk 88267 ▁onları 88267 ▁office 88266 ยอม 88264 空間 88261 otit 88260 ડિયા 88258 ▁káv 88257 ležité 88255 ລິ 88253 ▁Lj 88252 ▁өйр 88249 ▁deman 88248 ХИ 88247 ▁Lista 88247 ▁παί 88245 ▁परिणाम 88242 ▁framför 88241 ▁δέ 88240 ÃO 88239 ▁баяр 88238 ovina 88237 ρημα 88237 ▁забора 88237 pog 88236 ▁뿐 88235 ▁mbeidh 88234 ▁ಅಲ್ಲಿ 88232 ▁avuto 88231 ▁მწ 88231 уе 88228 িফ 88228 ませんが 88227 ▁പറയുന്നു 88226 vog 88224 ▁cantid 88224 ൊപ്പം 88223 ▁mégis 88223 ▁vërtetë 88223 齐 88221 ještaj 88220 ▁Ville 88220 可能です 88220 ជាង 88219 ▁asiakka 88219 ▁Politika 88216 ሂ 88215 ▁lala 88214 ▁nájdete 88212 ▁व्यापार 88211 ▁Képviselő 88210 ▁tàu 88209 حاب 88208 ▁Nye 88205 ▁locu 88201 рват 88200 ▁cald 88198 ၾကည္ 88197 চ্চ 88196 ymmen 88195 ្មាន 88195 ക്കാൻ 88193 ▁mię 88193 ▁කඩ 88192 ▁acesso 88190 ▁aspekt 88190 žív 88188 ודש 88188 ▁forklar 88187 ▁înd 88187 ਸਿ 88185 sı 88184 ▁Wiki 88184 ▁ฉ 88183 സില് 88182 ▁fich 88182 ्यातील 88181 ▁המט 88181 ▁سائ 88180 ગો 88179 ▁cenas 88178 ▁pristup 88178 ▁उद् 88178 ▁פלא 88177 ▁възраст 88175 ▁ටිකක් 88175 ▁Ohun 88174 1/ 88173 ▁cid 88173 ▁wody 88173 jsť 88172 ▁خيال 88172 chine 88170 เขต 88170 ▁зөр 88169 ▁hakan 88168 ▁agarup 88165 ▁zina 88164 ▁Lokal 88163 ▁Minggu 88163 ▁manca 88163 ▁prese 88163 ヨ 88163 ávan 88162 สมัย 88162 ▁körny 88162 ▁ساختمان 88161 ▁rovn 88158 ▁uq 88157 ▁нашим 88157 ▁بسي 88155 ▁droš 88153 නී 88151 ▁ടീ 88151 ▁simpli 88150 ▁sonr 88148 ▁PDP 88147 ▁данной 88146 вната 88144 ▁процесу 88144 ▁noastră 88143 trú 88141 вере 88141 ▁послуг 88141 ფილი 88140 ▁کوچک 88135 ريس 88133 ▁zgjidh 88130 ▁ਮਹਿ 88130 ▁ନାମ 88130 ▁країни 88127 ▁색 88127 லக 88126 ▁Paw 88126 ਖਿਆ 88125 ▁synt 88124 ▁еже 88124 ប្រជា 88123 ▁ambazo 88122 ▁malé 88121 غر 88120 ▁hatrany 88117 ▁Хе 88117 ▁kiy 88115 ▁gemeinsam 88114 ▁පමණක් 88114 ▁дія 88113 ▁ابتدا 88109 ▁පිය 88105 եխ 88098 ▁xếp 88096 ▁Αθήνα 88095 ▁필요한 88091 ▁Interna 88090 ▁שמורות 88088 ▁مکان 88088 КТ 88086 ▁lide 88086 ▁său 88084 튼 88084 ▁kW 88083 ▁कहानी 88081 ίδες 88080 ▁قهر 88080 ▁хэн 88079 ▁buona 88078 ಿದ್ದರೆ 88077 ▁membros 88077 ▁tempos 88074 zük 88070 ▁منهن 88070 rock 88062 ampani 88060 insel 88059 ▁slob 88059 死亡 88059 առնալ 88058 ோடு 88058 ▁γυ 88058 ▁дунд 88052 立ち 88052 ▁Rä 88051 ▁mãi 88051 ▁shiga 88051 而言 88051 गो 88049 ▁תורה 88049 ▁ವಿಚಾರ 88049 ▁гур 88048 大學 88048 ুণ 88046 ▁Comisión 88043 ▁samping 88042 ▁গত 88042 ահան 88041 至少 88041 ▁июля 88040 მდე 88038 puh 88036 ▁mou 88036 ▁umshwalense 88034 ▁രണ്ട് 88034 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▁cerv 87932 子的 87932 ▁зао 87931 ңгі 87925 ▁encant 87925 ánya 87924 ▁акаде 87923 ▁ទំព័រ 87923 理论 87922 ▁न्यूज 87920 ▁Урал 87917 ▁ಒಂದ 87917 istis 87916 ▁destul 87916 ▁ДЕ 87915 ▁izraz 87913 ▁በማለት 87913 ingos 87910 ▁Syar 87910 ▁معصوم 87909 ▁відео 87907 ဟုတ္ 87906 dog 87905 ರಿಸಿ 87905 ▁ponúka 87903 ▁Cip 87902 ເຕ 87898 ໂລກ 87897 ▁zowel 87897 ▁morg 87896 ▁nesi 87896 ▁dje 87895 ilist 87891 zta 87891 ▁formular 87891 ▁132 87890 ▁ਕਰਨਾ 87889 ▁ਰਹਿ 87887 ▁študent 87885 ▁сәнғәт 87883 прыемства 87882 ▁상태 87881 ▁ubbing 87879 deu 87877 Play 87876 ▁imibono 87876 ▁فیصل 87876 ▁Ment 87872 ▁있다는 87872 ▁อาหาร 87871 hne 87868 ▁člena 87867 ▁indiv 87865 ▁όλο 87862 ▁necesita 87861 ungguhnya 87859 ▁သတင္း 87859 ▁ujum 87855 ienne 87853 ▁لوی 87853 чым 87852 agnos 87850 ▁kandungan 87850 ▁істе 87850 ❤ 87849 лез 87848 ▁بہتر 87846 yaning 87843 നില് 87841 เผย 87841 ▁frans 87841 뉴 87841 ▁Optim 87840 ▁бүс 87840 ▁Tämän 87839 시면 87839 ▁Prema 87837 ▁mulher 87837 ▁Ата 87835 ▁συνέχεια 87834 ινή 87833 ▁talla 87833 独立 87833 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87731 ▁тіл 87731 較 87731 بدأ 87729 ▁настоящее 87725 ▁ganin 87724 ələrini 87723 ▁Kontrol 87721 ▁ورت 87721 ▁ਦੇਸ਼ 87718 ент 87714 ▁fayy 87714 ▁RI 87713 ▁Zusammen 87713 ପରେ 87712 ▁нөх 87712 ▁برگزاری 87710 PH 87708 мара 87707 ▁Kath 87706 ▁вашата 87705 aging 87704 ▁loin 87704 ုတ် 87701 ▁ತಪ್ಪ 87700 ▁ness 87698 슬 87698 ▁kaiken 87697 eshëm 87695 ▁پوري 87690 ▁гүйцэтг 87687 ▁iklan 87686 ▁පොලිස් 87686 ڪلي 87684 ▁kelib 87684 ▁опози 87684 ▁clàraichte 87683 brev 87680 ▁للن 87679 angaz 87678 න්ඩ 87678 ▁عرف 87677 ▁Ð 87675 க்கிறது 87674 ▁Haupt 87671 ▁diyaar 87670 ंदर 87667 item 87666 ၌ 87666 ▁disposizione 87666 ▁αύ 87665 ທີ່ສຸດ 87664 ▁ເປ 87663 ▁Ted 87662 ▁importanta 87662 edaan 87661 ▁Elis 87661 ▁مجبور 87661 zah 87660 ียด 87659 ▁mogen 87659 느 87659 ပင္ 87658 áro 87656 食物 87654 ▁ikusi 87651 ▁Més 87650 álya 87648 ▁mopakigbahin 87647 ▁bajar 87645 ▁ngalang 87645 ▁lilla 87644 cijske 87642 စြာ 87641 ▁slimming 87640 თამაშ 87639 ▁drepturile 87639 ләрен 87638 ▁lazım 87637 虚 87637 ▁مدار 87635 外国 87632 værk 87630 yriaus 87628 ▁деңгей 87627 ▁متخصص 87626 iriki 87625 ▁लिंक 87625 iteen 87623 ▁şehir 87623 ▁መግለጫ 87623 ledes 87620 ▁בדי 87618 ೂರ್ಣ 87616 ▁σώ 87609 ▁विरा 87608 ▁började 87603 lığ 87602 ▁inşa 87599 ▁tjen 87599 ▁föränd 87596 ческую 87595 byn 87593 ▁говорят 87593 íti 87592 ▁müt 87591 vironnement 87590 ▁fonda 87588 ▁kursus 87587 ▁норматив 87587 ▁kommunen 87584 ▁verand 87584 ▁Čes 87583 ▁szczę 87582 ZO 87581 ▁fantasti 87581 ▁problemat 87580 ከል 87579 asting 87576 ถนน 87574 ▁pulak 87574 rè 87573 យើង 87570 ▁коментари 87569 ▁وصل 87569 орі 87568 ▁гаруй 87568 ▁राजनीति 87559 ยนต์ 87558 ▁przem 87557 ▁الأمير 87557 çãeste 87556 тырға 87555 ▁ожида 87555 بحث 87554 ▁beti 87553 ▁eer 87553 ▁familiares 87548 ▁ynd 87548 ▁μπορού 87547 ▁espe 87545 ▁teles 87545 ▁യാ 87544 ▁naiz 87543 ▁нисек 87543 ▁میلیارد 87543 ▁coti 87542 ህአዴግ 87541 ▁veç 87541 ▁habitual 87540 щини 87539 venil 87538 alib 87537 ▁ساخته 87537 ▁Bros 87536 कले 87535 keä 87533 äyt 87533 ▁රය 87532 plý 87530 ▁Георги 87528 ▁участка 87527 意外 87527 auj 87525 ωσης 87525 ▁romanti 87525 ▁". 87524 ▁erano 87523 ▁bä 87522 ▁подчер 87522 ▁Awan 87520 ▁الاتحاد 87520 øring 87519 ▁Тәүге 87518 ▁tisti 87516 ▁ஷ 87511 ваю 87508 ▁gegr 87507 ▁reaks 87505 ریق 87503 ▁pose 87503 iring 87500 ▁bruges 87500 ▁stö 87500 ▁язы 87499 ▁Room 87496 ▁KT 87494 ▁maskin 87494 ▁정책 87492 ▁виру 87491 ▁йому 87491 orius 87490 ▁לגבי 87489 ▁כס 87487 usus 87486 ラム 87485 ित्त 87484 쿠 87483 علن 87482 टु 87480 éndose 87479 ндары 87479 Ast 87478 ेद 87478 olc 87477 ▁viti 87476 itej 87475 ▁dă 87474 هما 87473 ▁مرتبط 87473 anco 87469 ▁lliter 87468 ▁נכ 87468 ülü 87467 ▁viera 87467 समा 87466 ▁îşi 87466 ▁kolon 87465 ▁ಅಧಿಕಾರಿ 87465 ခ်စ္ 87464 ▁කිරීමේ 87464 ▁deltag 87460 ▁പറയാ 87460 ▁wasaar 87459 ▁wpis 87458 liwe 87457 лаар 87457 ▁ຖືກ 87455 цького 87453 ரச 87453 ▁empez 87453 ▁fart 87453 ▁yatırım 87452 ▁confor 87450 ▁cif 87446 ▁tād 87445 ▁제가 87445 ▁princi 87444 ▁sahəsində 87444 ▁tamm 87444 ▁számít 87443 ▁שוב 87442 anaí 87439 ▁978 87438 ▁рачу 87438 ▁دوب 87438 ▁منتقل 87437 obne 87436 ▁SOS 87436 ▁함 87433 aną 87431 uci 87431 ницата 87431 ▁bə 87428 ▁poeta 87427 ▁feri 87422 ونية 87420 ▁혼 87418 ▁অফিস 87417 льства 87415 ▁ପୂର୍ବବର୍ତ୍ତୀ 87413 ▁সম্ম 87411 UJ 87410 ▁Atau 87408 ▁ushbu 87407 ▁tjenester 87403 ▁Àir 87402 ▁négy 87401 တော့ 87400 ▁ຂະ 87398 ▁Esim 87395 拜 87394 ▁විස 87393 ▁nampak 87392 ▁üy 87392 ▁jednostav 87390 冒 87390 ▁čast 87382 ▁період 87382 mies 87380 ▁Gene 87380 زع 87379 ▁ქუჩ 87379 onisa 87378 ▁fotografia 87377 AKT 87375 רוני 87372 علم 87372 ▁bati 87371 ▁തിരു 87371 ▁subtropikal 87370 IONAL 87369 ▁ład 87369 ၽ 87368 ▁ľud 87367 -198 87366 ▁medan 87366 immer 87365 ▁benö 87364 ▁Ate 87363 ▁Archit 87362 ▁ഇന്ന 87362 ▁simpat 87359 aifa 87358 ▁felé 87357 ▁பர 87356 ganya 87355 ▁intereses 87353 րույց 87352 ▁Pria 87351 ▁υψη 87349 ▁примі 87348 ▁Cic 87347 posit 87346 gər 87345 ධානය 87345 ตาย 87345 ститу 87343 ▁einzel 87343 ▁ліку 87342 ▁Jú 87341 ikio 87340 داول 87339 唐 87339 ▁Bursa 87338 ▁અનુ 87338 网上 87338 ▁बाहर 87336 وانید 87335 ▁ውጤ 87332 ▁បោះ 87329 кав 87326 ▁miért 87323 ▁ունեցել 87323 벌 87322 ▁расположен 87319 ▁hạnh 87316 ▁Spro 87315 ▁ମୋଦି 87315 Ţ 87314 ברו 87313 ▁ronn 87313 眼睛 87313 ЛУ 87312 akisa 87311 ავად 87311 ▁Gona 87311 ▁ዐ 87310 ศิล 87308 ▁cais 87307 ləy 87305 ▁değildir 87304 ▁անվ 87304 ▁стъ 87301 ▁لگا 87301 निया 87299 ቸዉ 87299 ▁කළේ 87298 ▁Saf 87296 桌 87296 ▁diketahui 87295 unica 87294 ▁Gracias 87292 ؤال 87288 дном 87287 ▁adatok 87284 ▁σκέ 87284 ▁μεγάλο 87282 ▁elast 87281 ▁perl 87281 ವರಿಗೆ 87280 ▁dijadikan 87278 тол 87277 ▁step 87277 ▁ಪದ 87277 giu 87276 ערכת 87276 ▁zeigt 87276 ▁មេ 87273 isialta 87271 ▁вихо 87271 ▁cierto 87270 ▁органів 87270 Љ 87268 Pas 87267 ▁pric 87266 ▁özellik 87265 jske 87262 boi 87261 nast 87260 жени 87260 ▁məqal 87260 ିତ୍ର 87259 ดอก 87258 ▁Ponte 87258 ▁vzor 87258 ▁razmiš 87257 ▁ଗତ 87256 ылды 87255 ▁увагу 87254 ćem 87253 ▁wind 87252 ▁Тру 87252 dach 87248 ▁Bidh 87248 asiat 87246 ▁ijtimoiy 87244 ▁nezakhi 87243 ▁Soft 87242 ▁பிடி 87241 ▁avanta 87239 entan 87235 upas 87235 ▁membre 87234 agang 87232 ▁divul 87232 ▁1.1. 87229 ▁blom 87229 anjang 87228 ينو 87228 င္႔ 87228 ▁hasonló 87228 ▁Xin 87227 ▁бур 87226 lof 87225 ▁izmantot 87224 ▁منظر 87223 חלט 87221 ▁ਮੁਕ 87219 umpang 87218 ▁zbir 87216 ▁बनाउन 87216 퇴 87213 ଂଚ 87209 ▁semmi 87209 ▁ոստիկան 87208 ೫ 87205 ▁Prag 87205 ▁eher 87205 ▁tratt 87205 ▁prostora 87204 ▁Теперь 87203 ▁bày 87202 ▁tiv 87201 ▁πί 87199 ▁ietek 87196 ▁oyisisekelo 87194 ▁derecho 87193 ▁돈 87192 یری 87191 ▁dlh 87189 ▁ferro 87187 ▁wê 87186 ▁മല 87185 ▁engan 87180 ▁مبارزه 87180 ograph 87176 เยอะ 87172 ▁jawatan 87170 呈 87168 ▁мүмкіндік 87167 АВ 87166 ▁күтәре 87163 တဲ့အ 87161 ▁terp 87161 访 87159 ▁Latin 87157 ▀ 87156 ▁Luật 87152 ▁ફરી 87152 moc 87150 ชนิด 87149 ▁articole 87149 ▁days 87148 ▁kryesore 87148 ▁বিশ্ববিদ্যাল 87147 ▁výstav 87146 ▁تاریخی 87146 க்காக 87143 ▁noslē 87143 achuun 87142 ▁aday 87142 ▁aspek 87142 몰 87142 пас 87140 ▁Jama 87140 ▁специја 87140 ▁учнів 87140 ▁wewe 87139 储 87138 ▁წელი 87133 ▁läht 87129 ▁редо 87128 ▁कार्यक्रममा 87126 ▁ලේ 87125 ustes 87122 ▁dată 87118 ▁ਅਮਰੀਕਾ 87116 ▁tzv 87115 ▁رڳو 87115 ▁olv 87114 ຊ່ວຍ 87113 ▁щ 87113 bior 87111 isé 87110 ▁स्पर् 87110 thon 87109 テレビ 87105 ▁Dù 87104 ▁прво 87104 ▁berl 87103 ▁maranéhanana 87097 ▁зөвшөөр 87097 ▁nieuws 87096 ikela 87094 ▁تنا 87093 его 87092 12) 87088 ▁σχετικά 87086 asach 87085 ารา 87085 ▁aard 87085 ▁emit 87082 ▁possui 87081 隐 87080 ▁hå 87078 ▁miljo 87077 ▁araşdır 87076 ▁boven 87076 ▁Skol 87075 稳定 87075 ▁разі 87072 မ်ဳိး 87071 ▁jäm 87071 ▁mf 87070 สนาม 87067 ▁여기 87065 ვიდ 87063 ▁Ев 87062 ▁നന്ദി 87062 കളുടെ 87057 ▁გამოს 87057 ▁nového 87055 ▁bảng 87054 ▁Gum 87053 ▁özellikle 87053 ंधी 87052 ▁سياست 87052 ▁катастро 87050 ▁40, 87048 ▁spise 87047 ▁गर्छ 87046 からは 87044 ですか 87044 agat 87043 üll 87043 ęku 87043 ▁صادق 87043 ▁izaz 87041 ▁malih 87041 ineet 87040 ▁edərək 87040 icen 87038 ▁själva 87038 ▁səh 87037 ▁харилцаа 87037 ▁Mudah 87036 ▁tehnologi 87035 ▁εβδομά 87033 ▁Depois 87032 ਗਤ 87030 дности 87028 ▁vonatkozó 87028 ▁أر 87028 ▁Haji 87025 ബൈ 87023 ▁departe 87022 ▁pilsētas 87022 ▁вакан 87022 ▁хөгжлийн 87022 ▁නේද 87020 ციალური 87019 ▁Kampung 87016 ▁угро 87014 niego 87013 ▁متحدہ 87012 ▁نخواهد 87012 ▁vuole 87010 ▁Ведь 87005 ▁Dě 87003 ুট 87001 遊戲 87001 ሺ 87000 ▁opć 86999 ▁بكل 86999 ▁изку 86998 ység 86997 ▁отличи 86997 ▁háb 86996 श्वर 86995 ន្ធ 86991 encie 86990 бур 86989 ▁دیتے 86989 ▁විශාල 86987 कट 86986 ▁solución 86986 ▁Industr 86985 ▁ಮತ್ತೆ 86982 ▁alır 86978 บุคคล 86977 နီ 86977 ▁arom 86973 170 86972 ▁언제 86972 ▁해외 86970 ▁έργ 86967 ▁knjige 86964 発生 86964 ▁amba 86963 ▁Viet 86962 ▁κάποια 86960 ể 86958 をしている 86958 ▁тэх 86956 ▁धर 86956 க்கிற 86954 ▁NB 86954 ▁bestuur 86953 روي 86952 ▁oportunidad 86952 ▁അയ 86952 ▁izatea 86951 ▁ಜಗ 86949 ▁tocar 86946 jeru 86944 њен 86943 ▁ಅದರ 86942 ▁Sebastian 86941 ▁dość 86941 ▁visiem 86939 lərindən 86937 ▁několik 86937 เหนือ 86935 降低 86934 ંત્ર 86933 ▁Халық 86933 จ้าง 86932 Air 86931 ▁Tokyo 86931 ▁salir 86929 ▁ասում 86928 ▁തിരിച്ച 86928 ▁sonke 86927 alisht 86926 ▁gwamnatin 86926 ▁HAPA 86920 ▁rutin 86920 ▁Silver 86919 ▁المصري 86919 ▁prilo 86918 ▁менедж 86917 ▁ٹو 86915 YD 86914 ▁Neu 86910 ▁مستوى 86907 øse 86904 χύ 86904 ▁ಬು 86903 föld 86902 ▁작품 86902 ▁migliore 86901 ▁ଲା 86901 ▁Prä 86899 projekt 86898 ▁mpaka 86897 akay 86895 ▁2.1 86895 ▁Gunung 86895 ▁አዋ 86895 ▁नभ 86893 スタッフ 86892 ▁möjlighet 86891 ಿಗಳು 86890 ▁llawer 86887 ▁ਚੁੱ 86886 idig 86885 ບົດ 86885 ራቸው 86885 اتها 86883 ▁virk 86883 പോലെ 86882 ustega 86880 ▁block 86877 ▁الجه 86877 лии 86876 ፉት 86876 ▁järgi 86874 ベル 86873 ▁Dream 86872 zimmer 86871 ▁BD 86870 ▁prive 86870 ებრივი 86869 빛 86868 ▁እንዳይ 86866 телем 86865 我就 86864 ▁beteken 86863 ▁1.4 86862 ljenih 86860 ДП 86860 ▁shaxs 86859 ▁ေသ 86857 ▁ګډون 86856 ▁feest 86855 ▁dichi 86851 ▁центар 86851 ▁akholwa 86850 κός 86848 ▁ბან 86846 ണിക്ക 86843 ▁châu 86840 eksyon 86838 ភ្នំពេញ 86833 ▁päälle 86831 ▁oktat 86827 ្ច 86826 ▁semble 86826 INN 86821 ▁Stein 86820 的主 86820 ▁ತರ 86819 ▁ətraf 86817 ែត 86814 េញ 86812 ▁əməliyyat 86811 ▁بمن 86811 јар 86810 ījums 86809 ▁Ie 86805 forderungen 86804 ▁vang 86804 ▁інтэр 86804 ▁delar 86800 ▁kanta 86800 beri 86799 гчид 86797 ▁seotud 86797 ▁belli 86796 ▁ایف 86793 term 86791 ▁álbum 86791 ▁5,5 86790 ▁Einsatz 86787 јна 86781 ▁tans 86781 ▁Wald 86780 golo 86778 ▁DK 86778 ▁hins 86778 ▁держави 86778 steder 86775 IAL 86774 andó 86773 ▁undang 86771 ▁clu 86770 ▁والص 86770 ▁dəyişiklik 86765 ▁itholakale 86764 ▁sucesso 86763 ▁mənə 86758 adia 86756 ▁určite 86753 ▁покра 86752 umn 86751 nić 86750 ▁مخکې 86750 ▁gevoel 86748 ▁projekte 86747 삼 86747 vau 86746 ▁דברים 86746 ეზ 86743 ▁kasashen 86742 ▁јазик 86742 واک 86741 ностей 86739 ▁Sprooch 86738 ▁درآمد 86737 թի 86736 ▁சென்னை 86736 ▁רפוא 86735 روج 86734 ▁кезінде 86732 ▁ගැට 86732 ▁ժամ 86731 ▁keuntungan 86730 วัต 86728 уро 86727 ▁මන්ත් 86726 edett 86725 veriş 86724 ▁koniec 86724 entang 86718 ▁spennende 86718 ਾਦ 86717 اسة 86715 ત્રી 86715 ▁tanta 86714 ▁cate 86713 ▁Llingua 86712 ▁hjir 86712 ျခား 86710 ▁rendez 86710 ▁ਸੰਗ 86710 ტები 86709 ▁людям 86708 ▁03. 86706 ▁Krem 86705 ▁ammat 86704 ▁Naala 86700 рица 86699 ▁кажется 86698 łoż 86691 ▁도시 86691 ▁ඉල්ල 86689 áma 86688 ▁Rodríguez 86687 ▁ходе 86686 ලික 86685 ▁ölkəm 86685 ▁감사 86685 ▁መዝ 86684 /, 86683 λίου 86682 pping 86681 ▁konser 86680 ▁przeg 86675 ▁גייט 86675 ▁ማህ 86675 ėž 86673 ▁còps 86673 мый 86672 ▁وہاں 86672 ▁ବ୍ୟକ୍ତି 86672 ▁mëdha 86670 essant 86663 ājam 86661 niga 86660 이어 86657 йылған 86655 ▁srit 86655 ▁조금 86655 ▁већи 86653 iceáil 86652 ुप 86648 ▁schlecht 86648 ▁pago 86645 ▁боломжтой 86645 臨 86645 ▁korang 86643 hell 86642 ▁tonga 86639 ▁κάτω 86639 ▁маста 86639 ▁ہفت 86638 聘 86637 үүдэд 86636 ▁haus 86636 ▁mootummaa 86636 mọ 86635 áre 86635 ▁Canon 86633 ▁Punkt 86633 टने 86632 ▁іде 86632 зана 86630 ▁Šia 86630 ▁비교 86628 ▁varmasti 86625 ▁odol 86623 ▁velo 86621 ▁રહ 86621 mí 86620 ▁Saar 86620 ▁기타 86619 ▁sleg 86617 涨 86611 ashara 86609 ūti 86609 ▁سوالات 86608 שראל 86607 ▁hennar 86607 ▁suks 86607 ▁obt 86606 ▁روم 86604 ▁latviešu 86601 ▁farm 86600 imhne 86599 ▁percorso 86599 ægt 86597 ▁antic 86592 ▁обі 86591 ▁flyt 86589 ▁Очень 86588 ▁asawa 86586 ▁włos 86586 PER 86585 ▁صحبت 86585 ▁Được 86584 ▁כפי 86582 හස 86580 hwe 86577 ▁Юлаев 86577 ▁creativ 86576 ▁yilda 86576 ▁prinsip 86575 ▁подготов 86574 ▁aspoň 86571 ▁թող 86570 ▁વર 86570 ▁Nha 86569 ▁22.0 86568 ▁CIA 86568 ciós 86566 ▁Custom 86566 ამართ 86561 咨询 86561 ▁ஆசிரிய 86560 зів 86559 ▁Usta 86559 ▁स्कूल 86559 abanye 86558 ough 86555 ▁председа 86554 စစ္ 86553 ▁serviciu 86551 ▁llet 86548 ▁nela 86547 ▁Olimpi 86545 ▁Hø 86542 ▁батыр 86541 ▁Tao 86540 ▁Nisan 86539 ▁sofort 86537 ▁በጥ 86537 ladan 86536 ແຕ 86534 ▁zadrž 86534 ائز 86533 ▁хеҙмәткәр 86531 ñen 86529 ▁වැට 86527 対象 86526 vezető 86525 ▁startet 86524 ligst 86523 йня 86523 альное 86522 ▁skill 86520 accord 86518 ப்படி 86518 тырыу 86517 ▁nout 86517 ▁edelleen 86516 ▁next 86516 ▁ସ୍କ 86516 šak 86515 روط 86515 ▁vay 86514 ▁1600 86513 ▁Kuh 86513 ▁للد 86512 ▁bức 86510 ▁yìí 86509 اڻي 86505 ▁Friday 86502 ▁aru 86499 ೀತ 86494 Fin 86493 ▁cerem 86491 ▁یہی 86490 ▁hôn 86484 ใบหน้า 86483 ▁өтті 86483 ▁alternatief 86482 ▁partíu 86481 ▁mơ 86479 ▁Mohamed 86478 ▁masina 86478 andin 86477 ▁បើក 86477 ▁Kao 86474 λικ 86472 varo 86469 ჩვენ 86469 ▁Пад 86469 ▁znajd 86468 จิต 86466 ▁ć 86464 wenn 86462 ▁Malam 86462 ▁svom 86461 ▁קבוצ 86461 ▁sje 86460 ▁ท่าน 86460 GG 86459 iisul 86459 ▁éppen 86459 чния 86453 ▁ኃይ 86452 ▁kirjut 86450 ▁ፖሊ 86449 ▁espectac 86446 ▁täyd 86445 ▁mabe 86444 ССР 86441 ▁Amazwi 86438 ▁រួម 86437 ▁Koko 86436 orban 86432 ღი 86431 ▁അന്വേഷ 86430 ▁Suur 86429 ▁авсан 86427 ▁شناخت 86427 ▁திரும 86427 ▁trúc 86426 рое 86424 ▁Anfang 86423 wuh 86422 ▁মৃত্যু 86421 сели 86419 τούμε 86416 мыг 86416 ▁ঘট 86414 合わせ 86412 ▁kaominina 86411 ▁إدارة 86407 ▁richteg 86406 ezte 86405 ▁deoseb 86404 æf 86399 ▁chwil 86397 ▁chiqarish 86396 ▁slaap 86395 ▁συνεργ 86394 ▁идеи 86394 ▁Herriko 86391 ▁Pues 86390 ▁mohl 86390 ▁nani 86390 ▁Acum 86389 ▁příspěv 86389 ▁በዓ 86389 akta 86387 ੈਲ 86387 ▁движения 86386 ▁unan 86385 ңә 86381 itav 86380 ▁ამერი 86380 վեն 86378 ▁verste 86373 vuoti 86371 ▁គ្រប់ 86371 ใช 86368 ▁hanes 86367 ▁Просто 86367 ▁šok 86364 ▁المعلومات 86363 ▁Republica 86362 ▁arren 86360 ▁bee 86360 ▁пытан 86359 ின்ன 86358 stroj 86357 พลัง 86357 ▁continuación 86357 ▁материалов 86356 GL 86352 เฮ 86352 ▁सिर्फ 86352 uddin 86349 ▁மூலம் 86349 ▁eyni 86348 ભાવ 86346 ▁حالی 86346 න්නා 86344 ▁umb 86343 胞 86339 ប្រាស់ 86337 ▁formazione 86336 ▁hord 86336 許多 86336 ▁доктор 86335 儿童 86335 ▁menger 86333 ▁казино 86331 ▁침 86331 ഷ്ണ 86329 ▁çıkan 86323 laget 86320 qja 86317 ▁контроль 86317 ouri 86316 ایي 86316 ▁twe 86316 ▁rəsmi 86314 ▁попы 86314 unnen 86313 ▁Sinn 86312 ▁allra 86312 ▁nabi 86312 oce 86311 ること 86309 हरी 86305 หน่อย 86305 ▁Աստ 86305 hola 86304 ▁одговара 86304 ▁obx 86301 ▁taxa 86299 द्द 86298 ▁Кни 86298 ▁Roger 86297 ▁měsí 86294 ▁الفي 86293 に向 86291 ਾਈਟ 86290 สถานที่ 86290 ▁Lid 86290 ▁درې 86290 ▁Henri 86289 にして 86285 ymų 86282 ▁janvier 86282 рид 86280 ▁François 86279 ▁sinna 86274 ▁ناب 86273 ▁බැල 86272 ▁případ 86270 ▁eet 86266 ର୍ଶ 86265 ▁neka 86265 ▁Phong 86263 ▁Hå 86261 ▁echter 86261 ▁حذف 86261 ばかり 86261 更是 86261 ▁Beidh 86257 ▁kanker 86254 ▁ngokus 86254 angkah 86253 ▁gereg 86253 ▁qiz 86250 ▁நான 86249 ▁शुभ 86248 ▁חת 86247 haft 86245 ▁Ekonomi 86242 ▁webu 86242 ▁Llo 86238 ▁comarca 86238 ▁jön 86237 ▁швид 86236 ▁prawa 86235 ▁zatím 86235 ▁გუნ 86234 ▁სკო 86234 ▁accompag 86232 ▁dhal 86232 ▁קצת 86229 ▁teacht 86226 ▁БИ 86223 AMI 86222 ▁Бор 86220 СО 86219 ▁पर्यटन 86219 ป้องกัน 86218 ▁Música 86217 ▁donna 86215 ▁೨ 86215 ▁Söz 86214 ▁awa 86214 ▁nowych 86214 ▁ਸਕਦੇ 86213 ▁ക്കു 86209 сности 86208 ▁tölt 86208 ohle 86207 ▁holda 86207 ▁Hàng 86206 ▁оператив 86206 ຫະ 86205 ▁homme 86205 ▁ró 86205 zach 86204 ▁lawan 86203 ▁сезона 86203 cau 86202 ▁πέρα 86199 ▁لحاظ 86199 ប្រើ 86198 ▁pievien 86195 ▁посебно 86193 ▁महत्वपूर्ण 86192 ▁ngăn 86191 ▁سور 86191 ▁Kür 86189 ▁Wand 86186 ▁kültür 86185 ▁प्रो 86184 ▁성공 86184 휴 86184 ▁Nke 86178 Pres 86177 ▁πολλές 86169 akken 86167 ▁chans 86166 ▁ilaal 86162 ▁träd 86160 ▁כּ 86160 ▁Cash 86159 raidh 86156 син 86155 ▁päivän 86153 ▁teritori 86153 ▁ಪ್ರಕರಣ 86153 ทธิ 86151 vikling 86149 τσι 86145 ปก 86145 ▁upande 86143 ▁акция 86143 ▁پېښ 86143 ▁몰 86142 ▁100, 86140 ▁conversa 86139 ▁င 86139 ▁honor 86137 ▁AW 86136 ▁gumawa 86135 ▁pij 86135 ▁لنا 86135 ▁φορές 86134 ▁suor 86130 ▁வைத்து 86130 ங்களுக்கு 86129 ▁רצ 86129 ▁ડા 86129 miot 86127 ▁Mour 86127 ▁സ്പ 86126 ationale 86122 τυχ 86121 ▁монта 86119 资产 86119 ▁cyntaf 86117 امین 86116 ▁Genom 86116 eriya 86114 ficu 86113 misel 86113 ▁Hata 86108 לץ 86106 요일 86106 ண்டி 86105 ▁moes 86105 ▁Fómhair 86103 ▁kështu 86103 國家 86103 wości 86102 ▁своими 86102 წე 86101 ▁чином 86097 ▁(24) 86096 ▁verschidden 86093 ▁Lietuv 86092 ▁ہونا 86092 ▁katerih 86091 ondrana 86090 elian 86089 ▁bezoek 86089 เทคโนโลยี 86088 ▁ألف 86087 或是 86087 ▁ары 86086 ▁crítica 86085 ▁fér 86085 ▁tjän 86085 ชัด 86084 ▁Мак 86084 ▁ойлго 86084 ▁films 86083 ▁tươi 86083 ▁ترجم 86077 ▁skatt 86075 ▁Արմեն 86073 կար 86071 րոպ 86070 ▁ເທ 86068 ▁passion 86067 потре 86066 ▁사람이 86066 ▁மீது 86065 ▁เกี่ยว 86065 ▁ಎಂ 86064 ▁hitro 86062 னுக்கு 86060 ▁Goberno 86060 LU 86059 ▁近 86059 ويت 86055 ▁วิธีการ 86054 ▁berbagi 86053 ▁θέλ 86053 ▁ismert 86052 হার 86050 พรร 86049 ▁существует 86046 áy 86045 ▁გამოც 86044 ▁købe 86042 ▁ស្ន 86040 ▁clases 86039 มห 86038 ▁marr 86038 сквитки 86037 ▁Desc 86037 ▁شرط 86037 ▁چیست 86036 ▁ေရာ 86036 ▁bukur 86035 plaats 86034 ▁игре 86030 ▁तुल 86028 ▁Apl 86025 ▁soddis 86024 сък 86022 ▁መብ 86021 ▁enkulu 86020 ▁shamp 86019 ▁Cus 86018 ვარი 86015 시장 86015 ▁jantung 86011 idem 86010 ▁nød 86010 艾 86010 ▁Booking 86009 ヘ 86007 ▁Zaj 86006 ▁закри 86006 ▁brev 86002 ▁togel 86002 ▁pracow 85997 ▁περισσότερο 85997 ▁gwar 85996 ვად 85995 ▁adó 85995 險 85995 ▁very 85994 ▁bersih 85992 тельности 85991 ▁nastup 85990 ▁rason 85990 ▁uvid 85989 णात 85988 † 85987 ▁correct 85987 ▁Александар 85986 属于 85986 isté 85984 កាល 85983 ▁اللي 85983 ▁ovoj 85981 ▁бюдже 85981 ביט 85979 ▁Ante 85979 ▁unsur 85976 ▁ákv 85975 ▁ಬಳಸ 85974 τεχν 85967 ▁ámbito 85966 ိဳ႕ 85965 ▁küm 85965 ▁stere 85965 ▁тапсырма 85965 ▁licha 85963 ▁olungiselelwe 85961 ▁tárgy 85961 ▁යම් 85961 dlalo 85960 soo 85959 ▁täysin 85958 ▁Mittel 85955 ▁ehti 85955 ▁inani 85952 ▁гісторыі 85951 ▁adeil 85949 ▁તરફ 85948 frage 85947 ığınız 85945 ▁mielestä 85945 ▁انف 85945 ▁სახე 85945 없이 85944 ▁сыҡ 85942 ▁علا 85942 δεί 85941 ▁Verw 85937 ლეს 85936 ▁نئے 85934 ▁vsem 85933 ▁લખ 85932 ▁adag 85931 ▁trady 85931 ▁кезе 85931 orro 85929 ▁Vitamin 85929 ▁наук 85929 を知 85926 ▁facendo 85924 ▁Такси 85923 ▁Израи 85922 شق 85920 ▁Pula 85919 ▁součas 85916 згі 85915 ▁رسانی 85915 ▁зям 85908 више 85907 ▁पाणी 85906 ▁dalis 85905 ▁Jeho 85903 ▁dezvolt 85903 ▁vertel 85903 ▁okungena 85900 ▁Valm 85898 žiaus 85891 ▁користе 85890 ▁ғину 85890 ▁ngezidingo 85887 OTE 85886 ▁inaug 85886 antwana 85885 ▁menjawab 85884 ▁აპ 85883 ▁Радий 85881 ünf 85880 ▁okam 85880 ▁наказа 85879 istisk 85877 ▁puasa 85872 ▁senón 85872 ▁Interesse 85871 ▁osjeć 85869 ▁presid 85868 ▁जीत 85868 ▁سڄي 85866 ▁kuuluu 85865 пані 85862 ▁모르 85857 ▁dipaké 85856 ▁दोस्रो 85856 ▁Hercegovini 85855 ▁Spot 85855 ▁TUR 85855 ▁odi 85853 ▁corret 85852 වාහ 85851 adı 85847 tus 85847 મુ 85845 ▁Zwe 85845 ▁shtë 85845 ▁termini 85845 ▁ragaz 85844 Rep 85840 ▁pás 85840 пярэ 85837 ାମୀ 85837 økken 85835 ጠት 85835 ▁Kras 85835 ▁نگار 85835 ▁canlı 85832 디오 85832 ▁quiz 85831 ▁suurem 85831 entreprise 85828 ▁säin 85828 ▁ომ 85827 ซิ 85826 īju 85825 ▁dniu 85824 ▁договора 85824 ▁číslo 85821 ทําการ 85819 āra 85817 ▁tanya 85815 ▁tulis 85815 essu 85813 ▁vsaj 85811 ায়ের 85809 ▁سار 85809 ▁świ 85808 ▁ډیر 85808 ▁pourrait 85807 ▁escre 85806 ▁धो 85806 続け 85806 ▁meva 85805 色的 85805 ehto 85802 ▁melaksanakan 85802 ▁колико 85800 ▁olis 85799 eddeng 85798 ataka 85797 Sal 85795 ▁účast 85795 opak 85794 ▁Skul 85790 ▁heraus 85790 ತಿಕ 85788 ▁ביט 85787 mey 85785 በቃ 85785 ▁sakbay 85785 ▁মানুষ 85785 ▁Clinton 85784 ▁forv 85783 ▁skupine 85783 ೊಡ 85782 なくても 85782 ▁guten 85781 경제 85781 undleni 85779 ókn 85779 δικό 85778 জি 85777 ነጋ 85774 ▁өрг 85774 ▁ciri 85772 ▁koup 85771 ▁შენი 85771 ▁എത്ര 85770 ป่า 85769 ▁ingyen 85769 tà 85768 сје 85768 音乐 85767 GU 85764 ▁sakhe 85764 kve 85762 ▁πι 85761 ynku 85760 ແທ 85760 15, 85757 ibot 85757 ▁बल् 85757 ▁soda 85756 ▁საერთაშორისო 85755 بلي 85753 ▁еки 85753 ುತ 85750 ▁2,4 85750 ▁tók 85750 ▁Hasan 85749 ▁udvalg 85749 ▁государ 85748 нији 85747 ▁ยู 85747 刊 85747 ▁такође 85744 ▁čud 85742 ▁пресс 85742 ▁کرکے 85742 ▁vesh 85741 ▁inoltre 85740 [...] 85739 軽 85738 ▁Hé 85737 ▁എന്നി 85737 ▁sofa 85732 สิ่งที่ 85731 ▁yhden 85731 ▁ತೋ 85731 ▁segons 85730 íss 85729 lesen 85726 ▁διαθέ 85726 ▁impul 85725 метр 85724 ▁perspektiv 85724 RES 85723 ▁espor 85722 fru 85720 ▁transmit 85719 ▁obce 85714 ▁बच्चों 85714 ສິນ 85713 وزی 85710 ▁neap 85709 つけ 85709 ニュー 85708 ▁mwisho 85706 ▁Premio 85705 ▁Lever 85703 ▁sgìre 85703 ▁CSS 85700 ိုလ္ 85699 ▁przeb 85699 ▁Serge 85698 ▁Βασ 85697 ▁šios 85696 ▁podrob 85695 ▁xal 85693 2005 85692 ▁nero 85691 컴 85689 ▁zahtev 85688 ▁материали 85688 ▁Зу 85687 ▁modd 85686 скен 85684 ▁Maroela 85684 éter 85680 ▁української 85680 assar 85679 لز 85678 ▁прогре 85675 ▁CF 85673 ▁kesan 85673 ▁എത്തി 85671 ▁ນາ 85668 ▁близько 85667 홀 85667 گن 85664 ▁häerzlech 85664 ▁llibru 85663 ▁begynd 85661 ěst 85657 сове 85656 torno 85650 ▁თე 85649 kë 85648 ▁આવ્યો 85647 ▁прежде 85644 ▁गति 85643 ▁Khoa 85642 ▁Hz 85641 ▁қатына 85639 ▁घु 85638 ▁עדיין 85637 වරු 85635 ▁there 85633 පත 85632 ▁дена 85630 harị 85629 ραση 85629 ▁obráz 85629 ▁عملکرد 85629 ▁Pey 85628 ▁octubre 85628 ▁محبوب 85628 部屋 85626 iliar 85625 ЭР 85625 ▁خانم 85625 ▁효과 85625 ▁nope 85621 ▁şü 85621 ▁comh 85620 ▁nép 85617 ▁हवा 85617 質問 85615 必須 85614 ▁Town 85612 셀 85611 ▁SF 85602 ▁draug 85602 ▁erittäin 85602 ▁ungekho 85602 skóla 85597 ▁службе 85597 ugna 85596 ຄັ້ງ 85596 ▁perfil 85595 ▁ustan 85595 ▁utakm 85595 ▁icon 85594 ੱਚ 85591 ▁saling 85590 ▁tirar 85589 ▁jung 85586 ▁бързо 85586 štruk 85581 ▁Prod 85580 ▁odd 85580 ▁ஸ்ட 85579 ▁मलाई 85578 wech 85577 ▁vijana 85575 ▁стратеги 85575 опера 85574 ▁svete 85571 ▁ร่วม 85571 ▁DEN 85570 ▁उम्मीद 85569 ▁જેમ 85568 ▁Váš 85567 смотре 85565 baud 85555 ▁ಭಾಷ 85555 yaj 85554 ▁solution 85554 ▁isteyen 85551 ▁Bede 85548 ▁iw 85548 ▁هما 85548 бог 85547 ຄະນະ 85547 ▁trest 85547 SR 85544 ▁мероприятия 85542 ▁ചാ 85541 țional 85540 িক্ষ 85538 ▁tante 85538 ▁ترك 85538 ▁Ď 85535 ▁ийм 85535 dex 85534 ▁પસંદ 85532 ellaan 85530 ▁toimii 85530 ▁نوي 85527 ▁doble 85526 ▁wefan 85525 ▁انرژی 85525 ▁ເຮ 85523 ▁الأع 85521 wendung 85520 дила 85518 ඛ් 85512 ▁przyjem 85512 ▁પ્રેમ 85508 ▁ຊາດ 85508 ▁مسلمانوں 85507 ▁ويل 85506 amour 85504 ▁Siudad 85504 少年 85503 Ку 85502 ▁дели 85502 ▁โรคสะเก็ดเงิน 85502 ління 85500 ຄົມ 85500 3). 85499 ▁Nẵng 85498 ▁וועלכע 85498 ▁октомври 85497 entino 85495 गार 85495 ▁brīv 85495 ▁বিরুদ্ধে 85495 ▁더욱 85495 itable 85492 ▁muligheder 85492 ▁prieten 85490 ▁daardie 85489 laid 85486 ▁preparar 85486 зії 85484 ▁ador 85482 ▁seachad 85482 ▁încer 85481 राब 85480 ▁pierw 85480 ▁vilatge 85480 ▁ನಿರ್ದೇಶ 85480 ▁ბევრი 85480 ▁тың 85479 ▁Nina 85478 ▁imagen 85478 ▁хүрг 85475 ▁stör 85474 ▁முடியா 85474 mant 85472 ▁ஆகிய 85472 ▁למש 85470 ادگی 85468 ▁pennad 85468 ԱՄ 85466 acher 85463 ovolj 85462 ▁brill 85461 ؤس 85460 ▁중심 85458 ▁henkil 85457 ▁ሊያ 85457 öitä 85456 ▁megfelel 85456 ចាប់ 85455 ▁həbs 85455 IJE 85454 ▁pyet 85454 ▁제작 85454 یرہ 85453 мек 85451 ▁disip 85448 ▁İç 85447 ▁אנג 85447 шень 85445 ▁surf 85445 hant 85443 ▁acceder 85442 ▁viat 85441 μματα 85439 ืด 85438 ▁ഇവ 85435 ▁Newyddion 85434 ▁Laman 85433 ūz 85431 ▁greið 85431 ▁недели 85430 ▁ბრძ 85430 ▁хотите 85429 ▁रकम 85429 二十 85429 ixi 85427 šću 85427 әли 85427 ▁Gle 85426 ▁разби 85425 ▁Buku 85424 ▁Takže 85424 ▁тулы 85423 도를 85423 ▁кож 85422 ▁prostred 85421 ▁umogon 85420 ròyìn 85418 ▁pacientes 85418 inājums 85415 ▁അവരുടെ 85415 მით 85414 ▁szcz 85414 म्ब 85412 Ớ 85412 ▁बसे 85412 ▁munca 85411 ענען 85408 шња 85405 لاع 85404 ▁Jaz 85404 ▁behöv 85398 、2 85398 ▁veh 85397 ▁деклара 85395 lue 85394 ▁سامان 85394 ▁simplement 85393 引起 85393 ▁gjer 85389 ▁çərçivəsində 85388 ▁Mäerz 85387 ▁ezinikeza 85382 ▁چھو 85382 ▁toj 85381 ▁kowe 85380 ▁разам 85380 ▁ONLINE 85377 ▁szeptember 85377 ▁tko 85374 ▁നിങ്ങള് 85374 ▁sizi 85373 阵 85372 ▁atmosf 85371 ▁raho 85371 ▁بیت 85371 ▁ethile 85369 weyn 85366 ▁Bosna 85364 ▁والذي 85364 ▁행복 85363 ścia 85362 spunk 85358 ▁جمل 85358 ▁арала 85357 ▁કંપની 85356 ξουν 85354 ▁रोक 85354 2017. 85351 ждении 85348 ▁принад 85348 ▁convocatoria 85346 ▁hätt 85346 ▁viktigt 85346 ebilirsiniz 85345 ▁valore 85345 có 85344 ▁namreč 85344 ▁finales 85343 טור 85342 ▁ດີ 85342 ೀಲ 85341 ▁wik 85341 ▁näch 85340 ▁titel 85340 ▁किए 85337 urada 85335 lös 85334 ▁جمعه 85334 ▁नम 85334 ▁Aziz 85333 ▁harap 85333 甜 85333 บาย 85332 ▁Csak 85332 וכר 85330 östä 85325 ▁ľudia 85324 ույթի 85323 ▁দাব 85322 ▁Kraft 85318 ▁Subscri 85317 ▁ukuv 85316 ▁kha 85315 ▁NBA 85311 ▁autoc 85311 ಳಿದ 85310 ▁кноп 85310 ▁taxi 85309 írás 85306 DM 85303 ▁Về 85302 ▁ترکیه 85302 ▁دستی 85302 ▁បុ 85301 ▁kasama 85300 ▁ngươi 85300 ▁ով 85300 欢 85299 ▁хамтран 85298 ▁magari 85297 ▁pēt 85297 擊 85297 ▁лига 85296 klės 85295 ▁jinak 85294 ▁aduna 85293 ▁архе 85291 ▁erotisk 85290 ▁settembre 85290 ▄ 85290 ▁engang 85289 ▁bop 85287 ▁stie 85286 寝 85286 ▁инфек 85285 ▁پوری 85285 bold 85283 ▁ለም 85283 ▁pyör 85281 주는 85278 oliv 85276 ▁movimento 85276 ▁ਕਈ 85276 ikė 85275 ▁Provença 85275 ustatud 85273 ått 85272 ▁απαι 85272 futa 85271 ပေး 85269 ▁خش 85267 ▁ក្រុង 85266 niai 85264 ेड 85263 ▁ឥ 85263 ▁үҫеш 85261 ▁ठा 85257 ▁ਨਾਨਕ 85257 тельная 85252 ▁급 85252 hlo 85249 industri 85249 それぞれ 85249 ຊີວິດ 85248 ▁топли 85248 ▁оҫта 85247 мисли 85246 ้อม 85244 ▁Yesus 85244 ▁später 85244 ▁tudja 85244 ▁сваю 85243 ▁cuore 85241 ▁nwoke 85238 ▁holat 85235 owanej 85234 ▁બંધ 85231 edra 85229 ▁ऑनलाइन 85226 याँ 85225 ▁kalan 85225 ▁search 85224 ▁तस्वीर 85224 ▁новата 85221 ▁წარ 85221 ▁सूर 85220 犯罪 85220 ▁elämän 85219 ▁компью 85218 ▁რეს 85217 ▁Cinta 85212 ָה 85210 ▁ផ្ត 85210 ▁λέει 85208 ▁предста 85208 ivy 85206 ▁beper 85206 nine 85204 ையே 85203 rachadh 85202 dienst 85201 ināts 85201 কম 85201 ▁korišten 85201 ▁աստ 85201 ▁gesam 85200 ▁αλλαγ 85200 ելը 85198 ▁Eind 85198 Trans 85195 ▁njihove 85192 ▁KV 85190 ▁চট্টগ্রাম 85189 ▁colle 85187 vesti 85183 fnu 85182 қтау 85182 පය 85176 ענד 85175 ▁2015- 85175 ▁kungani 85174 ▁Giới 85173 ▁misteri 85173 ▁ണ 85172 landa 85171 ▁îmi 85171 цької 85170 ▁veiks 85170 ▁รอง 85170 سبب 85166 ▁hits 85165 ▁podpis 85163 tw 85162 ▁заявил 85162 junk 85161 ▁Contr 85161 ແຮງ 85160 ▁रवि 85160 ▁Nähe 85159 ▁ไม่มี 85159 ▁herbal 85158 ▁sacri 85157 ▁תודה 85157 别人 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80367 েইল 80366 ▁सीमा 80366 ▁leicht 80364 ▁sendt 80364 ▁rhannu 80363 ▁ਫਿਰ 80361 ▁-, 80359 ټې 80358 ▁voet 80355 skole 80353 ▁ಸಾಮಾಜಿಕ 80353 ▁കമ്മ 80353 zék 80351 まる 80348 ▁pregunt 80347 ▁आयोज 80343 ▁uspješ 80342 ▁13:00 80341 ▁escritor 80340 ▁Xuan 80339 ▁sociala 80339 ilaa 80336 lost 80335 annada 80330 лио 80330 ▁Игра 80330 ັດທ 80329 ▁розви 80329 ijini 80327 ▁godini 80327 waga 80326 ീര് 80326 ▁Single 80325 ▁mərhəl 80324 ▁Sao 80323 ▁væl 80323 ላው 80322 ശി 80321 往往 80320 ፈጸ 80318 ▁ngap 80318 ▁лева 80317 ▁තුල 80317 ▁المزيد 80316 ▁उठ 80316 ▁خوف 80315 ▁حض 80312 ▁обеспечи 80311 ▁свим 80311 ▁upl 80309 ▁thogail 80308 ▁ያለውን 80308 ▁прадстаўні 80307 ▁zostały 80306 ▁שום 80303 ▁vazif 80302 ρική 80298 Ger 80297 ▁Кам 80296 ▁Utama 80295 ▁продукта 80295 广告 80293 ▁Western 80292 ▁Сам 80290 ▁маңызды 80290 ollar 80289 怎麼 80288 ▁hökum 80287 ▁особо 80285 ▁grà 80283 ยะ 80281 ▁Συμβ 80281 varande 80278 っております 80276 ็บ 80274 ▁llista 80273 لیک 80272 ാലി 80272 ▁ongeveer 80272 ▁indywidual 80270 ויה 80269 ិកា 80267 ▁አማራ 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▁đẳng 80191 odik 80188 ролтай 80187 माथि 80187 ഞ്ച് 80187 ▁Boss 80187 ▁Ebe 80187 ▁ব্ল 80187 žena 80186 ေက် 80186 ▁izd 80184 क्रा 80182 ▁TB 80181 ttal 80180 ▁ለተ 80180 ▁suala 80179 ▁ավարտ 80178 中国的 80178 ▁نظامی 80177 ▁희 80176 สาน 80173 注文 80172 ▁заду 80171 ▁صورتحال 80170 낸 80170 ક્તિ 80167 ▁ито 80167 গ্রহ 80165 ▁ოთ 80165 ضية 80164 ▁souhlas 80164 ▁पठा 80160 ▁ولسوالۍ 80159 chin 80158 ▁məsələ 80158 Home 80157 ▁milions 80154 ▁დედა 80153 ▁ខែម 80152 ▁semn 80149 ▁hefði 80147 ▁खरी 80147 чкиот 80143 olada 80141 ▁नए 80141 avaa 80140 ▁mondiale 80140 ▁пласти 80140 šha 80139 ”), 80137 ▁ಸಾಧ 80137 ▁мэргэ 80136 म्बर 80135 ▁קה 80133 ▁२१ 80133 نیو 80132 ▁lits 80132 폴 80129 piť 80128 óa 80127 ▁امله 80126 ລົດ 80125 ▁ਸਮੇਂ 80125 ▁nämä 80122 reste 80121 ▁publicada 80121 ▁įsig 80121 ▁февраля 80121 atib 80120 хин 80119 ▁atent 80119 engin 80118 ▁ჩამო 80118 әнәғәт 80116 ▁teada 80115 ▁അവസാന 80115 ▁유지 80112 ▁parād 80111 allada 80110 ▁arrimaha 80110 rivelse 80109 病院 80109 krát 80108 ▁Karin 80108 ▁दिवसा 80108 ▁будете 80106 ▁dicho 80105 ▁Рі 80105 ▁لطفا 80105 ▁ezith 80104 ▁Bav 80103 ▁Sche 80102 ▁vlasti 80100 網站 80100 通信 80100 ▁Vores 80099 ▁bawat 80098 űn 80097 ለኝ 80097 ▁ine 80097 ▁riječi 80097 ▁៨ 80097 ilä 80096 хат 80096 ▁помі 80095 odat 80093 зербай 80093 ▁kennt 80092 ▁ਤਰ੍ਹਾਂ 80092 هداف 80090 ikkö 80089 ▁tău 80089 لاج 80088 ด้ 80087 ▁کندهار 80086 zelf 80085 ▁autant 80083 ligheter 80082 ▁урындағы 80081 შინ 80080 ▁graet 80080 ▁προκα 80079 ніі 80077 ედა 80077 ▁klientów 80077 ▁тогава 80077 塑 80077 ▁לוק 80076 хоор 80075 јн 80075 ▁económico 80075 ▁سندھ 80074 ▁pack 80072 elius 80071 ▁Ở 80070 ▁ରଖି 80069 ▁xug 80067 ▁коммер 80067 ▁sasao 80065 ▁تڏهن 80064 ▁samuti 80062 ▁προέ 80062 ▁капі 80060 ▁मंड 80060 cript 80059 ▁okug 80058 ンプ 80056 ▁Jenter 80055 ▁stalo 80053 ▁giugno 80052 ▁qayta 80051 ▁Rè 80050 ▁idman 80050 ▁yaklaşık 80050 jící 80049 éien 80049 ▁выбира 80049 듯 80046 grunn 80045 لیا 80044 ▁Bên 80044 ▁barát 80043 ਖਾ 80041 ▁වෙන්නේ 80041 eniami 80040 zando 80039 ▁SWT 80038 ▁javob 80036 ▁ਕਲ 80036 ▁そ 80033 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79935 ▁посред 79933 ▁अगाडि 79933 ▁Италия 79932 ▁لږ 79932 ▁reven 79930 isiNgisi 79927 ▁Hör 79927 ▁የተባ 79927 mentaires 79925 miar 79925 ҙәт 79925 parte 79924 ▁viesnīcu 79922 цийн 79921 மோ 79921 ▁glè 79921 ▁lähes 79920 まり 79920 ▁promoción 79919 ▁salaku 79919 ▁ٻاهر 79917 ▁միջեւ 79916 ର୍ଥ 79914 ▁Aller 79913 ▁ऐसी 79913 ▁PAG 79912 atorilor 79911 ⁄2 79911 yba 79910 рку 79909 ▁আওয়ামী 79909 ブラ 79909 ▁winkel 79906 ▁əlaqədar 79906 ierto 79905 onių 79904 льним 79904 chais 79903 คลิป 79903 อังกฤษ 79903 といった 79903 ▁apreci 79902 ▁përmb 79902 ذك 79899 ▁nadat 79899 ▁presi 79895 ▁позитив 79895 ▁frig 79894 ▁viso 79894 encial 79890 ▁1998. 79889 ▁pamię 79889 ignon 79888 ongeza 79887 ത്തേ 79886 ▁रिपोर्ट 79886 ▁ኩ 79886 ▁ഇല്ല 79884 antii 79883 ▁wypad 79881 nées 79879 ▁istri 79876 ▁saiki 79876 ▁ඉඳ 79876 pais 79874 ▁Ushbu 79874 ▁nosos 79874 ▁مشاه 79872 ▁svog 79870 ▁गौ 79870 ▁wabo 79868 ▁Μετά 79868 ▁pross 79867 ▁тауар 79864 सकाळ 79862 ▁dispositivo 79862 ▁zöld 79862 ▁قبر 79862 ▁arbetar 79861 ▁scurt 79861 ▁ša 79858 antzi 79855 ▁pridob 79855 ▁zmiany 79855 ▁Taas 79852 ▁псориазис 79851 andri 79850 ▁স্থ 79850 ▁שבה 79845 خة 79844 ▁lowo 79842 ▁masturb 79842 ▁Kej 79841 ▁майже 79840 ବଳ 79838 ปัจจุบัน 79838 ▁проду 79837 ▁ಇಲಾಖ 79834 ឈ្មោះ 79833 ▁yis 79832 后的 79830 ▁zegt 79829 iczny 79828 4). 79826 ▁градо 79826 ▁гаспа 79825 ▁Mientres 79822 You 79821 ▁ഒരാ 79821 ▁личности 79820 ▁DUN 79819 ▁produksi 79818 ▁Тро 79818 ▁ratu 79817 хээ 79814 ▁тэле 79813 ▁אונטער 79813 ▁міської 79812 дарҙың 79810 ତୀ 79810 ▁Свети 79808 ▁سبتمبر 79806 ▁menyang 79803 ▁Wales 79802 ▁yarar 79802 ▁футбол 79802 ▁koulut 79801 ▁1991. 79800 ▁trist 79800 តាំង 79799 illər 79798 ▁Idee 79798 ▁Mapa 79798 ▁rights 79798 ▁сталі 79798 ություններին 79797 ▁сметка 79797 ढे 79796 ▁최고 79795 prej 79794 ▁uffici 79794 encer 79792 र्जा 79792 ▁dint 79788 ▁sikap 79787 ▁Þessi 79787 ▁төгрөг 79786 ▁նախկին 79786 ▁सम्मेलन 79786 ▁amis 79783 ▁заро 79782 ▁1952 79781 ▁limita 79780 ▁belangrijk 79776 ▁Tattoo 79775 ▁escorte 79775 ▁म्ह 79774 တယ် 79773 ▁hyvää 79773 ▁Myös 79772 ▁States 79772 ▁pedir 79772 ینې 79770 าร์ท 79769 ▁Հետ 79769 ▁tsakanin 79768 ▁sponsor 79767 ▁skór 79766 허 79766 adhna 79765 ▁erreichen 79765 ▁profissional 79765 ▁atrast 79764 ▁čer 79763 납 79762 ▁occup 79760 ▁мясцо 79760 ▁செல்ல 79759 देव 79756 ▁œ 79756 ▁gce 79755 ungisa 79754 ▁således 79754 рява 79751 obay 79750 ▁Zach 79750 шэн 79749 ▁учиты 79749 없 79749 тифика 79748 ▁başlı 79748 ▁ನಾಯಕ 79747 ▁Educ 79744 տեղ 79742 ▁ondan 79742 ▁эше 79742 નાર 79740 ក្រុង 79739 ▁mnoh 79738 ▁Σά 79737 银 79737 ▁seko 79736 ▁temelj 79735 ▁өлкәһе 79735 ▁stranke 79732 戸 79732 urunkan 79731 ▁Gujarati 79729 ▁Resep 79729 şan 79728 ▁забу 79727 ▁Hansen 79726 ▁īpaši 79726 ينية 79723 უპ 79721 ▁करती 79720 ▁buv 79719 ▁1921 79715 ▁Sedangkan 79715 ▁███████ 79715 ▁ಲಕ್ಷ 79713 ▁ډلې 79712 ೨ 79711 ▁locales 79711 ▁Tunnisteet 79710 ▁ammoo 79708 ▁avald 79708 ▁mężczy 79708 ▁matat 79707 ▁क्योंकि 79707 ▁shfaq 79706 ▁пишу 79706 átku 79705 ▁atrodas 79703 ▁יר 79700 ▁zahval 79694 ▁Prop 79691 ▁progno 79691 ▁زرد 79691 ▁sayesinde 79690 ▁Garda 79687 ▁ویل 79687 ▁iespēju 79686 ▁kannattaa 79686 ▁kobi 79686 ▁seas 79685 ያዘ 79680 ▁katal 79680 ▁qorunur 79680 ▁gunung 79679 ▁Print 79677 ▁2017-2018 79676 ▁څل 79676 फ्ट 79675 ▁leth 79675 ▁соң 79675 ▁अध 79675 ▁Temmuz 79674 ▁seleccion 79673 ▁Entrada 79672 ▁Пробле 79672 gestellt 79671 ปะ 79671 ▁tamo 79671 ▁Drama 79670 ▁Nature 79670 ▁Anmeld 79669 ▁cử 79669 ▁orada 79669 ▁작성 79669 ▁tingim 79668 ▁říká 79668 ▁зголе 79668 ANGAN 79666 ▁ura 79666 ریس 79664 ▁Oro 79663 දය 79660 獲得 79659 ▁فساد 79658 несен 79656 ▁Kac 79656 ▁စစ္ 79654 മര് 79653 ▁clip 79651 جهه 79649 ▁आएका 79649 ▁ξενοδο 79648 ▁Player 79647 lanır 79646 ▁after 79645 ▁týžd 79645 imse 79644 ▁Bunu 79643 ▁transf 79643 ▁хя 79642 ତୁ 79641 ▁sajarah 79638 ▁Vip 79637 ▁tarehe 79637 แน่นอน 79635 ጠቅ 79635 ▁여자 79632 )! 79631 ▁constant 79628 ▁kelet 79628 ▁इसी 79627 ׁ 79625 ▁ಸಮಯ 79625 ▁нефть 79622 ▁нев 79620 它们 79620 ellow 79619 ▁واقعا 79619 ▁vijesti 79618 ▁Bia 79617 ebote 79616 ▁Сега 79616 णारा 79614 ljaju 79613 zny 79613 โรงเรียน 79613 ▁verander 79612 tery 79611 ▁irod 79609 ▁૩ 79608 ▁catro 79605 kania 79604 ▁डाउनलोड 79602 ▁بالنسبة 79600 毎日 79600 ▁ಆರೋಗ್ಯ 79599 ელმა 79597 ▁kaysa 79597 ▁bernama 79595 ▁ਦਲ 79595 ង្គ 79594 しては 79594 ▁razlog 79593 enciclopedia 79592 ثار 79591 ▁ulang 79591 ▁муль 79591 ▁ځل 79589 andry 79588 րում 79588 ▁ಎನ್ನುವ 79588 сак 79587 ▁trän 79587 ▁диза 79587 ρχεται 79586 行的 79582 ætte 79581 ▁Amás 79581 ▁Erot 79581 нени 79579 ▁Hof 79579 ▁rabat 79579 ቻቸው 79578 ▁Zal 79577 ▁kiệm 79577 ▁بواسطة 79577 ก่อ 79575 ▁ویکی 79575 ▁séier 79573 โฟ 79571 ▁зае 79571 utet 79570 ▁ajje 79568 ▁kumuntu 79568 ▁μέρα 79568 owanego 79567 ▁Ngọc 79567 ▁maggio 79567 なんだ 79567 ťah 79565 ▁тиҙ 79565 ituar 79563 ▁음악 79563 ▁uod 79559 ▁svůj 79557 ▁Hulio 79556 ▁ପରିବାର 79556 ▁Utara 79554 жие 79553 λλει 79552 երիկ 79552 ▁ću 79552 действие 79551 ▁बंग 79551 bym 79549 ▁establec 79548 ▁həmçinin 79548 ▁Нью 79548 ▁нагла 79548 ettre 79546 iliselt 79544 ▁Maleta 79544 ▁ildən 79544 ▁Wieder 79543 레스 79543 ▁Estos 79537 ▁тесто 79537 ▁кітап 79536 ▁nha 79534 ▁otázky 79534 cieron 79532 祭 79532 ▁inšt 79531 ▁қарай 79530 ▁classifica 79529 ▁gynnwys 79529 ▁invalid 79526 ▁hodiny 79525 runa 79524 阻 79522 ▁Lucas 79521 τσα 79519 ▁замі 79519 ▁hotellet 79518 ▁طرفان 79517 ▁দিবস 79515 ulega 79514 ▁Schutz 79514 ▁هستیم 79514 ▁ples 79513 ▁tehát 79512 ▁яму 79512 ▁certeza 79511 혈 79509 irro 79508 ▁materiales 79508 ▁دولة 79507 ▁Benjamin 79504 ▁Jamii 79504 ▁تلفن 79504 ßt 79503 ▁워 79503 ▁गरिने 79502 जो 79499 ▁געש 79499 ▁Install 79498 ▁Εί 79498 ▁بروز 79497 ភព 79496 ▁ਗ੍ਰ 79494 atit 79493 зација 79493 ▁يون 79492 eilge 79491 λεύ 79488 ▁אט 79487 itando 79486 reichen 79486 ▁hiri 79485 ▁septiembre 79484 ▁yaa 79484 ▁llegó 79483 ▁тоді 79483 烟 79482 ▁обнов 79481 ▁простор 79480 ▁рез 79478 ▁Çin 79476 хийл 79475 creacion 79474 naeth 79474 ଟୋ 79473 ▁perus 79473 ▁ବ୍ୟା 79473 वेळी 79472 ▁abụọ 79471 ▁quad 79470 РК 79469 въ 79469 ▁wraż 79466 ssch 79465 ေၾကာင့္ 79465 ▁કરોડ 79465 ▁Finfinnee 79464 出去 79464 രമായ 79463 ▁siiski 79463 ▁ሰላም 79463 ▁sollt 79460 uttu 79458 agun 79457 ▁span 79457 ▁النبي 79457 ▁רבות 79456 ▁MR 79454 ▁comentariu 79453 生态 79452 시키 79452 tikan 79451 zése 79451 ▁сожалению 79451 որի 79450 ▁կոմ 79449 ▁جامعة 79449 ဆိုင္ 79448 eneral 79445 bler 79444 感覺 79444 сене 79443 สนุก 79443 ▁вместо 79442 ▁KS 79441 ▁Russland 79441 ▁woo 79441 ▁ész 79441 Tha 79440 מאַ 79440 ▁hinaus 79440 ▁კარგი 79440 ruf 79437 認為 79436 ▁europea 79435 hajt 79434 ▁Cần 79434 ▁todell 79434 ämm 79432 ▁вак 79432 ▁نخ 79432 ▁próprio 79431 ▁ဆရာ 79431 adang 79424 ▁overras 79424 ▁Unters 79422 ▁Hep 79420 無法 79420 count 79419 ជើង 79416 ▁mū 79416 ▁خواه 79415 eddel 79413 yq 79413 ▁ημε 79413 ▁повинні 79413 occasion 79411 ▁пас 79406 milyen 79404 ▁segí 79403 ▁краса 79402 ▁оборудования 79399 कुल 79397 継 79397 séges 79396 ▁dësh 79396 ▁nettsted 79394 ▁буква 79394 ▁үҙәк 79393 ปลอดภัย 79390 ▁ਸਾਡੇ 79390 мія 79389 ▁φά 79389 ▁начале 79389 ibia 79388 ▁Yur 79387 ▁ezelőtt 79387 ▁áfram 79387 abilece 79386 ален 79386 ▁sedangkan 79386 ▁Dêr 79385 ▁થયા 79385 ▁Nyheter 79382 ▁tüket 79381 ▁рәйесе 79379 ▁thịt 79377 13] 79376 adies 79375 eleke 79375 ▁Ватан 79375 ▁gespe 79371 osla 79369 පො 79368 နွ 79368 hoa 79367 ▁správy 79367 טין 79366 実は 79366 ildik 79365 ▁Grupp 79365 ▁ימים 79365 ▁millions 79364 ▁Suite 79363 ▁öv 79363 日本の 79363 ▁szakmai 79362 तन्त्र 79360 ▁debut 79359 ธาน 79358 ୟମ 79357 ▁promover 79355 ▁মনে 79354 ▁März 79353 ▁культура 79351 ▁фарма 79351 üket 79350 សី 79350 ▁කෑ 79350 ▁crois 79349 ▁증가 79349 ▁Parque 79348 ATT 79347 ▁በር 79347 ▁miei 79345 ▁خپلې 79345 ಾವು 79344 ▁redac 79344 ▁väärt 79344 ▁koncu 79343 ▁مجاز 79341 리의 79341 ▁darbinie 79337 ▁nasayaat 79337 ▁Слобо 79337 ▁eM 79336 ▁spoluprá 79336 arei 79335 ▁pasaulio 79335 รายการ 79334 ▁Slovensko 79334 ▁geloof 79326 จบ 79325 ▁xer 79325 ▁mediter 79322 डू 79321 ▁Hugo 79321 ilerine 79320 ക്കൂ 79320 ▁sèlman 79320 ▁වැදගත් 79320 tide 79318 oker 79315 ▁أنا 79308 okas 79306 ▁വല 79306 ▁przeznacz 79305 україн 79303 ▁toimub 79303 pson 79302 ೇರಿ 79300 ▁kisebb 79300 ▁проме 79300 ▁jakin 79299 неры 79296 ▁problemen 79296 很大 79296 ▁gord 79295 ▁પાર 79293 ▁ζωής 79292 ▁skryf 79291 ▁bj 79289 ▁привлека 79289 有所 79286 ysa 79285 ▁Erfolg 79285 ▁નવા 79285 стки 79283 ▁ብለ 79281 ราคาถูก 79280 ▁təq 79280 ▁SIN 79279 ▁veeb 79277 ▁поступа 79277 dris 79276 ቂያ 79276 ▁Polska 79274 meyen 79272 ▁मानव 79272 ▁ವರ 79272 ▁սովոր 79271 ▁ដើ 79269 ▁모바일 79269 ▁stranica 79268 ደራ 79266 ▁Tinggi 79265 ▁ស្រី 79265 ńskiego 79262 ▁danum 79261 ▁положение 79261 azh 79259 总统 79259 ▁۱۴ 79256 ▁sytu 79255 ▁szob 79255 ▁сақтау 79254 วบ 79253 rapport 79251 ຮູບ 79251 ▁grau 79251 နှင့် 79250 ▁Kết 79250 ▁تامین 79250 ▁удо 79249 ▁Mediateca 79248 ▁nuotra 79248 好了 79248 rosoft 79247 ▁Oui 79246 ▁вчи 79246 innin 79245 bakan 79244 ▁gjithashtu 79243 ▁cuestión 79241 ▁laure 79241 ಮಿಕ 79240 ▁grafic 79240 째 79238 mány 79237 bred 79234 ▁Conta 79234 ềm 79233 ▁yenidən 79233 ▁ajatt 79231 ▁ਪੁ 79230 ဖြဲ႕ 79229 ▁настрой 79229 ▁tovar 79228 ▁1916 79227 ▁Ευρωπαϊ 79227 ▁vorbe 79226 ▁libres 79225 ▁ambil 79224 ▁sabemos 79224 ▁fråga 79221 ▁priamo 79221 ▁سالم 79221 anam 79220 ▁ଗାଁ 79220 ▁Kwakhe 79219 ▁asian 79217 álta 79216 ಗಾರ 79216 ▁Pertama 79216 ▁naviga 79215 ▁сөй 79215 픈 79215 налите 79212 ▁Fyr 79212 gunos 79209 ▁하지 79209 ন্য 79207 zate 79206 ▁menys 79206 ▁lắm 79205 ધ્ય 79204 ობაში 79204 ▁zespo 79204 ▁veckan 79203 ysgu 79202 ▁атты 79202 ▁entonces 79199 жении 79198 جال 79197 ▁marrëvesh 79197 ▁Дирек 79196 ▁kluba 79194 ▁inar 79193 ▁варі 79193 isyo 79190 тория 79189 ▁ရှ 79189 тувати 79188 ▁наяв 79188 ڪيٽ 79187 ▁perda 79187 ▁белгілі 79187 ▁በው 79183 網路 79181 goo 79179 ▁Azken 79179 ▁Patrim 79179 ▁kjøpe 79178 렸 79178 ▁deven 79177 ▁oleva 79177 ▁ria 79177 ▁Verd 79176 ksz 79174 დებოდა 79165 ▁house 79164 ırda 79162 ার্থী 79162 ▁spēles 79162 家长 79162 үүдийг 79161 ▁Δι 79159 ▁dear 79157 ▁waardoor 79157 ▁tofauti 79155 ▁በሚል 79154 Form 79153 ▁neach 79153 ▁situação 79153 ▁BEL 79152 ▁স্ম 79152 əkk 79150 ▁hens 79150 られない 79149 tale 79148 ▁akak 79148 ▁घेऊन 79147 ▁Univer 79145 ▁comentarios 79145 มาร 79142 ▁zetten 79142 ້ຍ 79141 ▁ι 79141 ուզ 79140 ▁AFP 79140 ▁Ինչպես 79139 ▁samarbejde 79138 ▁stali 79137 ▁capacidad 79134 ▁کوچ 79134 ▁निका 79133 чика 79132 ▁DET 79132 lendi 79131 ▁arx 79131 אור 79130 јер 79129 ▁भाई 79128 ▁vandag 79127 anom 79123 czę 79121 ▁genuen 79120 ▁Hassan 79119 ▁nesti 79119 회를 79119 պետ 79118 遅 79118 ▁modele 79117 evol 79115 iteach 79115 ▁ਜਿੱ 79114 ▁កូន 79112 ▁Domingo 79110 ▁יק 79109 ▁Крім 79108 ▁ಪ್ರದೇಶ 79108 很快 79107 ຜະລິດ 79106 ▁alunos 79106 ▁ಕೋಟಿ 79106 ▁doru 79105 ▁acho 79104 ▁posky 79104 ▁αφορά 79104 ▁каква 79104 ▁মার্ 79104 ▁Γε 79103 uben 79102 eliti 79101 ▁dena 79100 ▁Duniya 79099 ▁animais 79099 ▁volledig 79099 плы 79098 ▁велики 79097 ▁যাবে 79097 ▁Emir 79094 ▁adım 79094 ▁baris 79094 felt 79093 ạng 79093 ▁матэрыя 79093 èng 79092 tente 79091 ▁vienmēr 79090 sd 79089 ▁enfin 79087 chaffen 79085 ▁функции 79084 daji 79080 ▁babes 79080 ▁மட்டும் 79078 ▁않을 79073 amde 79072 gañ 79072 ▁ғилми 79072 ▁brasile 79070 スポ 79068 成员 79067 ▁таш 79066 ▁Lil 79063 ▁acerca 79063 ▁resur 79061 ▁بلاگ 79060 کلی 79059 ienė 79058 vió 79058 ▁domic 79058 ▁kalla 79058 ▁saldır 79058 ▁solat 79057 صيل 79056 ସୀ 79056 ล่าง 79056 ▁Step 79056 ▁desenvolvemento 79055 ▁māks 79055 ▁проекты 79054 ▁priva 79053 eteg 79051 ▁maipanggep 79051 ▁дзяцей 79047 ពារ 79046 ▁рядом 79046 ▁cuidado 79044 ▁ଯଦି 79044 ▁syarat 79042 ▁síos 79041 ▁نکن 79041 ▁Kuba 79039 ▁ган 79039 ▁주소 79037 uldu 79036 ▁Broad 79036 acara 79035 ▁terg 79035 ▁vjeç 79035 elise 79034 ▁korpor 79034 ▁বৈ 79034 argent 79031 frika 79029 ▁uutta 79026 成长 79025 ▁mlade 79024 ▁Mär 79023 ▁ngawe 79022 撮影 79021 ▁කාලය 79019 。) 79019 alarına 79016 ▁sinc 79016 abes 79011 ▁dets 79010 ▁ВМРО 79010 atek 79009 richten 79009 ▁משמעות 79009 是什么 79006 ▁видите 79003 наче 79000 ▁Stil 79000 ▁વધારે 78999 ▁bawang 78998 ithiau 78996 ▁розумі 78995 ▁Lefase 78994 чными 78993 висо 78992 ▁conquist 78991 ▁Fram 78989 ▁rode 78988 ▁історії 78986 ▁() 78985 ▁deebi 78985 ▁miles 78985 ▁reale 78985 elih 78979 ▁Tac 78979 illende 78977 ▁невозможно 78976 ▁삭제 78976 мост 78975 જ્ઞ 78973 ଧା 78967 ▁Bravo 78967 ▁армия 78966 ▁түүх 78966 ▁किलो 78965 ibono 78963 ▁اضافہ 78963 nuti 78960 kultur 78958 issez 78957 ▁prijed 78956 toota 78953 ▁Ngesithi 78953 ▁закін 78952 èo 78950 ▁sosiale 78950 ردو 78949 ന്നും 78947 ▁এমন 78947 ▁пункта 78945 alf 78944 规范 78944 ▁TG 78943 ▁Ezikhethwe 78941 ▁parque 78941 Кон 78940 BM 78939 ▁Vah 78938 进行了 78938 ▁haqda 78933 چھا 78932 odiy 78931 élé 78931 єр 78931 ሰጥ 78930 joy 78929 不错 78929 ස්ක 78926 ▁Chin 78926 ▁udr 78926 ▁අගමැති 78926 labor 78925 ▁bov 78924 пити 78921 ▁удар 78920 ▁tanti 78918 γκα 78917 дик 78917 อุด 78915 ▁המדינה 78914 atsh 78911 ▁דאנארשטאג 78908 ▁gehi 78907 ್ರೀಯ 78906 ▁kesalahan 78906 ▁विस्तार 78906 ▁odch 78905 ▁ханга 78905 ແຫນ 78904 ▁분야 78903 omboka 78901 ▁ризи 78901 ▁PROD 78900 ▁එත 78900 ▁частности 78899 ▁vybav 78897 ▁baze 78896 ▁jep 78896 ▁תגובות 78896 ▁deutlich 78893 ▁fyrsta 78892 ▁sivu 78891 良好的 78891 ▁ღმ 78890 olof 78884 ▁dalyv 78884 peci 78883 unkat 78882 ▁Cari 78882 ▁напу 78881 ecektir 78880 ੱਬ 78879 ▁signi 78879 ▁өдрийн 78878 ▁scr 78877 ▁meninggalkan 78875 ▁mori 78875 untut 78874 ▁ገል 78873 ▁popis 78872 ▁የሀ 78872 ▁propaganda 78871 ▁Варна 78871 oros 78870 ▁tarvitse 78870 ding 78866 ▁taller 78866 দা 78865 ▁ଗ୍ରାମ 78864 เตรียม 78863 itéiten 78862 ▁bong 78861 ▁Lama 78860 ▁kemungkinan 78859 ▁Essa 78857 ▁aktuellen 78856 泊 78855 ▁дэлхийн 78854 ਰਨ 78853 ▁taruhan 78853 一路 78853 ▁jeed 78852 ayıb 78849 ▁dhib 78847 ▁органов 78845 google 78844 κολου 78844 研发 78843 ▁ngắn 78842 ามา 78841 ▁чад 78841 ▁Ming 78840 reds 78838 ▁mahdollista 78838 ▁nedan 78837 셜 78837 ▁Morg 78836 ▁୯ 78833 ▁jobban 78831 ▁ಸ್ಥಾನ 78830 Ste 78829 otę 78829 ▁التس 78829 ृद्ध 78825 ▁ಸೌ 78825 oniu 78824 ▁Jens 78824 ▁uneg 78823 ▁среда 78821 ▁näyt 78820 ▁докумен 78820 ▁voyage 78818 ▁іске 78814 ▁њи 78814 XT 78812 ເຊຍ 78812 ▁vene 78811 ▁सोम 78811 ▁уні 78809 ▁Ecco 78806 ▁qər 78806 ▁hyv 78803 ▁Міністер 78801 ▁ਗੁ 78801 ▁tso 78800 ▁ጋ 78800 ▁但 78800 мові 78799 بری 78799 ▁gốc 78797 ▁उहाँ 78797 ስጥ 78796 ▁жағдайда 78796 িতা 78795 ▁Izingxenye 78795 ▁выход 78793 ▁Diss 78792 ▁Nid 78792 ▁henni 78790 antayo 78788 ▁eit 78788 ▁mogę 78788 ▁แผ 78788 ▁afaan 78787 ▁entsp 78787 ▁આં 78787 ▁പാര് 78787 amies 78785 ▁lega 78785 ▁omkhulu 78785 ▁propon 78785 wil 78784 ələrinə 78783 ▁մակ 78783 ▁પગ 78782 智慧 78782 ▁duidelijk 78772 ▁pobre 78772 ▁тест 78772 aksan 78769 ▁Penn 78765 ▁NR 78763 誤 78763 reru 78762 ▁prosím 78761 jaid 78759 ግዚአብሔር 78759 ▁ඇයි 78759 ▁Dywedodd 78757 ▁એવી 78757 ▁აღარ 78756 อนด์ 78755 ማይ 78755 ▁представители 78751 ▁помош 78749 ▁jaso 78748 까요 78748 ▁私は 78747 ▁eya 78746 女子 78746 ▁ક્ષ 78745 采取 78745 ▁mladi 78744 ▁лъ 78744 伍 78744 ▁Kuch 78743 äksi 78742 ▁Daugiau 78742 ▁citas 78741 ▁puedan 78739 ▁Жан 78739 と同じ 78739 ▁wanaagsan 78736 ญาณ 78734 ▁zapewnia 78734 ▁برداشت 78734 ▁irá 78732 ▁ვიდეო 78731 ▁люд 78730 ▁नाव 78729 oday 78727 ▁ngak 78727 twitter 78726 自信 78726 ▁អាយុ 78725 来了 78725 थी 78724 дою 78722 ▁Gora 78722 ▁wspom 78722 басы 78720 ▁Брита 78720 ▁EH 78719 ▁plin 78719 ▁тада 78718 പക്ഷ 78715 ▁अपडेट 78715 ▁обору 78714 ೊಳ 78712 ▁vrijednosti 78709 ▁obez 78707 িও 78706 ▁শিশু 78706 ▁artikk 78705 ▁Territ 78704 onic 78701 ยืน 78701 така 78700 ▁geograf 78699 ▁kinder 78698 šių 78697 க்த 78697 ▁આધ 78695 ▁රිකට් 78695 ▁maioria 78694 ▁նիստ 78694 ▁iaith 78693 알 78692 ▁применя 78690 ▁МУ 78688 چھے 78687 ▁жыццё 78687 ▁♦ 78687 ግኘት 78683 ▁siyah 78682 ▁범 78682 ATS 78681 ▁grafik 78681 ▁tii 78681 իծ 78680 ▁daleko 78679 श्चित 78677 uksena 78676 rion 78675 ▁Penger 78675 ▁октяб 78673 ગુ 78672 чват 78670 ▁dweud 78669 ▁indiferent 78669 ▁bhaint 78668 cznych 78667 ผลิตภัณฑ์ 78667 ▁Links 78667 ▁נכון 78667 सह 78665 khe 78660 ▁خد 78660 됐 78658 ▁ផ្សេង 78656 ವಹ 78655 ▁બાળ 78655 ▁Devlet 78654 ▁мілі 78654 么 78654 ▁Meta 78653 ▁ട്ട് 78653 ▁deutschen 78652 ▁alf 78651 ▁sugan 78651 ▁lekar 78650 ▁предложе 78647 ▁средство 78647 ▁kusuka 78646 ▁socio 78646 ungkin 78644 ▁mũi 78643 ▁думку 78642 ▁നടന്ന 78640 机制 78639 czo 78638 рған 78638 ▁යයි 78636 ajari 78635 ▁hamkorlik 78635 ▁דארף 78634 ▁ಉತ್ತಮ 78633 ▁dewasa 78631 ▁získa 78630 ▁pinakamahusay 78629 skeið 78628 ajnë 78627 ▁माझ 78626 ▁iTunes 78625 patrimòni 78624 ▁ûn 78624 ▁ଜଳ 78622 ▁dienos 78619 ▁ქვეყნის 78619 ▁Kér 78614 ▁הרש 78613 ismos 78609 וויי 78608 ጠይ 78607 interès 78606 ▁Öss 78605 ▁هيون 78605 ▁uusia 78604 ▁allar 78603 ▁parlamentar 78603 ▁sait 78602 ▁terbuka 78602 aneen 78601 ▁اقتصادي 78601 ▁Sondaqui 78600 扬 78599 ▁kuth 78596 ត្តម 78589 ▁Nò 78588 ▁poziv 78588 jedno 78587 ▁Esse 78587 atada 78586 ărilor 78586 եւս 78586 radh 78585 ွန် 78585 ▁شریکولو 78585 ▁telekom 78583 ınca 78582 ριστε 78581 ACE 78580 ▁nimeni 78580 ▁जीव 78579 ▁Tiranë 78578 ▁buong 78578 kelige 78577 чину 78577 မွ် 78577 ▁مهاج 78577 ▁impacto 78576 ටිය 78574 ▁Европе 78572 ▁Nicola 78571 ▁zdjęcia 78570 тям 78569 ிருக்கிற 78569 ▁моно 78568 ▁cắt 78567 πουλος 78566 венство 78566 ▁ntle 78566 ▁daydi 78565 ▁Falls 78564 ▁enhver 78564 aith 78563 ▁ακολου 78558 ▁Kig 78556 ▁afya 78556 すご 78556 məyə 78553 šiel 78551 ▁你 78551 ▁joo 78548 ▁ಗೌ 78548 ▁sinabi 78546 qaal 78545 ▁мәғ 78543 ▁flyg 78542 すぐに 78541 SKA 78540 ኸ 78540 ▁boeken 78540 ▁പോസ്റ്റ് 78540 tiert 78539 ▁kass 78539 ▁rač 78539 ▁ご 78539 ▁cercet 78538 ▁Мәҡәлә 78537 ▁skoraj 78535 ▁Mij 78534 ▁kech 78534 ▁zade 78534 ነበረ 78531 ▁Yoo 78530 ▁Νέ 78529 ▁julk 78527 昔 78525 ходзі 78524 ▁Makan 78522 ▁obsz 78522 amazwi 78520 ▁Μπ 78520 ▁alegr 78519 应当 78519 சம் 78517 ▁තොරතුරු 78516 ▁Уладзі 78515 ▁сигурно 78515 ▁განმავლობაში 78515 ▁отдыха 78513 amnan 78512 ▁wêr 78511 ukir 78510 ▁କନକ 78508 ▁cult 78506 ▁legisla 78506 不了 78506 чением 78505 ▁KOR 78505 ▁انځور 78505 ▁لذا 78502 өрийг 78501 děl 78497 ▁боҙ 78497 ▁ingl 78495 ▁Орта 78495 警察 78495 ▁propios 78494 ▁ፖለቲካ 78494 ധിക 78493 ▁directamente 78493 ▁Матери 78493 ▁ģimen 78492 хал 78488 ▁Ramos 78487 ▁viloyati 78487 ▁buru 78485 ▁dzīv 78484 ▁حڪ 78484 ▁kontaktu 78482 ▁ਸਿੱ 78481 ▁annab 78480 ▁60. 78479 ebas 78478 ▁yau 78478 ▁ڪرا 78478 ▁דיר 78474 ▁KES 78473 ▁llarg 78473 бл 78472 ▁Gemeins 78472 ▁продължи 78472 ▁פּאָ 78472 აბი 78470 ▁reduz 78470 ▁กรุงเทพ 78470 っくり 78470 ▁dreptul 78468 ▁ymchw 78467 jatë 78466 ▁ɗuum 78466 resse 78465 ได้เลย 78464 ▁Ման 78464 rement 78463 өргә 78463 ▁lietuvių 78463 ▁crema 78462 ▁jiné 78462 ▁sytuacji 78462 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76555 ഫി 76550 ▁മനോ 76550 ▁Дні 76548 τιο 76546 imala 76545 ковод 76544 ▁касается 76543 ▁ಶಾಸ 76539 ▁sáb 76537 ▁Guide 76536 ဳိ 76535 ▁ች 76535 зори 76533 ▁приз 76533 ▁իրավիճ 76533 ▁plás 76532 ▁цене 76532 ▁māc 76531 ▁پنځ 76531 เวล 76529 ▁novinar 76529 ▁төсли 76528 ▁ervar 76526 ▁kehittäm 76525 .[3 76524 ▁Wali 76522 ▁ужо 76522 vég 76521 ▁vervol 76519 ▁bashk 76517 ▁essenti 76517 ▁Велико 76516 ▁Napole 76515 ▁waawi 76515 ▁دغو 76515 Educa 76513 ▁chega 76510 arụ 76508 ▁rust 76508 ▁آيا 76507 ▁ବର୍ଷା 76506 ▁lắp 76505 LF 76504 reira 76501 ▁årene 76501 ▁vís 76500 ▁Држа 76499 0000 76498 ஸ்க 76498 ▁szczegól 76497 频 76496 ▁Puh 76490 ▁operat 76487 empor 76483 acją 76481 ▁acid 76481 bana 76480 ▁السياسية 76479 ▁alábbi 76476 ▁خبري 76475 يارة 76474 ▁Styr 76474 ▁रचना 76474 概念 76470 ზნ 76468 ▁Metall 76467 ▁Zij 76465 ▁dışında 76463 ▁krop 76463 ▁zvlá 76463 ▁četr 76463 ▁બોલ 76463 ោម 76462 ▁কেন্দ 76462 ▁otev 76461 ▁quotidi 76461 եքս 76460 ▁성장 76459 দান 76457 ▁הופ 76457 ▁मार्च 76457 рою 76455 ▁σημα 76455 ▁Հար 76455 ▁Sài 76454 ดีต 76451 უმი 76451 ▁توی 76451 ▁وارا 76451 ciano 76450 redig 76450 aliana 76449 ▁εγώ 76447 ▁ჯან 76446 angas 76445 ▁muutt 76443 yxat 76442 ▁соблю 76442 ▁ეროვნული 76442 ▁블로그 76441 ▁Linh 76440 ▁საერთ 76439 ▁таба 76438 ▁כתב 76438 ▁Claude 76436 ▁grupper 76433 ▁ottenere 76433 ▁41. 76432 ▁stär 76427 ▁jolloin 76426 打造 76425 qaa 76423 ▁esam 76423 ▁muiden 76423 вању 76420 ▁hlavně 76416 ▁framkvæm 76415 aquel 76413 ▁Ҡара 76413 ▁کوتاه 76413 ▁Alice 76412 ▁ствари 76412 ▁roimh 76411 ▁činnosti 76411 ▁Радио 76411 мына 76408 だけでなく 76408 tiq 76407 ρήσεις 76407 ▁védel 76407 бәр 76406 насти 76405 ▁poñ 76404 ▁κοινό 76404 ▁פיר 76404 ▁SUA 76403 頁 76401 világ 76400 چہ 76397 ▁ұйымдастыру 76397 ▁Prifysgol 76396 ▁Парла 76396 ▁považ 76393 aporkan 76392 ▁odloč 76392 ▁بغیر 76392 지역 76392 useks 76391 ▁foreg 76390 -2012 76388 ▁եւս 76388 ▁További 76387 ▁لکه 76387 bru 76386 ▁অবস্থ 76385 ▁élmény 76383 स्थान 76382 នៅក្នុង 76380 ▁നഷ്ട 76378 ▁ঈ 76376 ненні 76374 president 76372 ზრ 76372 ▁ക്കാര് 76371 끌 76370 keer 76369 χείο 76368 ▁erhver 76365 вался 76363 ▁Nob 76363 ▁eye 76357 数量 76355 ▁decembre 76353 ▁éagsúla 76352 ▁divendres 76350 ▁пропози 76350 instal 76348 ▁reise 76348 ກອງປະຊຸມ 76347 ▁później 76347 ▁Trot 76346 ▁टोली 76346 зывают 76343 ▁indul 76343 重视 76343 ▁momente 76342 ▁146 76339 ▁ayah 76339 ಆರ್ 76337 reba 76336 ▁civiliza 76334 ▁joves 76334 ▁poveste 76334 ▁კარგად 76333 ▁स्मार्ट 76331 ▁maimaim 76329 ▁erbjud 76325 anjutkan 76324 ▁alates 76322 ▁geweld 76322 ▁15-20 76320 ▁ਬਲ 76320 ੋਨ 76318 ուստ 76316 ▁gestuerwen 76316 ▁نکته 76315 ▁дзіця 76314 statt 76313 ▁تعاون 76311 ladığı 76309 နီး 76309 ▁تعریف 76309 akaran 76308 ▁Господ 76308 regat 76306 ▁pussy 76306 ▁uphupha 76306 中小 76305 동안 76303 ነስ 76302 ▁иннова 76302 ▁atong 76301 hièr 76298 ▁kapsamında 76296 γκόσμ 76295 ▁випадку 76293 лалын 76292 뜨 76292 فات 76288 ድም 76287 ▁վերաբեր 76285 ▁lëtzebuer 76283 おすすめ 76280 腰 76279 ▁Miami 76275 ▁Ferrari 76274 ▁ағы 76274 ▁နှ 76273 ▁visión 76272 kinn 76270 ולוגי 76270 ੱਦ 76270 ▁obtenir 76270 桥 76270 ▁Pola 76269 ▁cóm 76269 ▁fatti 76267 eshe 76265 ▁forne 76265 ▁большин 76265 okwa 76262 ailleurs 76261 மீ 76261 ▁río 76261 ▁dono 76259 ▁Exist 76258 ▁ճանաչ 76258 edni 76255 ▁Ži 76254 ▁misschien 76251 ▁صالح 76251 ល្អ 76249 ▁Paket 76249 ▁carreira 76249 도로 76249 ▁informācijas 76247 vient 76246 ▁Alberto 76242 ▁ضر 76241 ▁HOT 76240 ▁Αι 76240 ▁जल् 76236 зок 76234 صلاح 76234 ീല 76234 ว่าง 76234 ▁حدیث 76233 ለማ 76231 ▁ቦ 76231 стрів 76230 ยู 76229 ▁proposéiert 76229 organisation 76228 žiui 76225 чность 76225 ▁масшта 76224 ▁Перед 76223 ▁агра 76222 ▁ғи 76222 taande 76220 ▁sociedad 76217 ▁vins 76216 ਚੇ 76215 bill 76214 ▁jende 76214 ▁hedd 76213 ▁nagta 76213 ատի 76212 ▁Onlar 76212 ▁riferimento 76212 ▁गोष्टी 76211 ▁atenção 76210 ζό 76209 ▁MUN 76208 俗 76208 ometer 76207 kú 76206 nolo 76204 ▁رغم 76201 ▁impli 76200 tigh 76198 ▁Sila 76198 эў 76197 ▁njani 76197 ▁விட்டு 76197 ▁Баян 76193 ▁सहमति 76192 ▁പ്രണ 76192 ▁xam 76191 ▁залі 76191 有一个 76189 把握 76184 octubre 76183 λές 76183 ИС 76183 ▁Mú 76177 ▁පල 76177 ▁tukaj 76175 劳动 76175 ▁Penc 76174 щина 76173 ▁چيف 76172 เหล่า 76170 ▁1922 76170 ყიდ 76169 добре 76168 ▁नव्ह 76168 욕 76168 kuk 76167 ▁দেশের 76167 ▁ಬೈ 76167 ▁ຈັດ 76167 ▁vore 76165 ብት 76164 ▁Jor 76164 ▁Hamb 76160 ▁उहाँले 76160 ▁Weihna 76159 ▁lás 76159 ▁lumi 76154 ▁arian 76152 ▁szy 76152 ▁બેઠ 76152 czor 76151 rådet 76147 惜 76147 ▁Zaman 76142 ▁rask 76137 ▁sgr 76137 ▁(23) 76136 වන් 76135 ▁Admin 76135 ▁situazione 76134 ▁ეკ 76134 aigua 76128 ubani 76128 lix 76127 ோர் 76127 ▁veröffent 76127 ▁diterima 76122 ▁ryb 76122 ▁អនុ 76122 ▁البا 76121 ▁शिक्षक 76119 원을 76118 ೃಷ್ಟ 76117 ▁quiero 76116 ▁половина 76115 haji 76110 ográfica 76110 uttet 76110 ХУ 76108 クリック 76103 ▁Sina 76102 ▁Assim 76100 ነትን 76097 wyddo 76095 ▁előre 76094 ▁নারী 76091 ຍາກ 76090 варот 76089 ▁Sätz 76089 önnum 76088 ▁últimas 76088 ▁өгөх 76086 里面 76085 ▁보내 76083 स्टर 76082 ▁устра 76080 도의 76079 ícies 76078 acile 76077 ▁سفید 76076 țile 76074 баса 76074 yíu 76073 بیر 76072 ذب 76072 ▁дала 76071 ▁ganti 76070 ▁πιθαν 76070 ipadas 76069 ▁տրամադր 76067 ▁әүҙем 76066 ▁माँ 76066 ▁бүтээл 76059 ▁역사 76059 ▁Portug 76058 ერმ 76056 adres 76054 ▁azərbaycanlı 76054 kraft 76053 ▁sten 76053 ▁huelen 76051 讨 76051 ▁upr 76047 泡 76046 ▁dign 76045 ▁២០១៨ 76045 uton 76044 ▁ମନ୍ଦିର 76044 ▁anten 76043 ▁برداری 76042 ▁አመራ 76041 ▁convirti 76039 ▁socia 76039 ▁악 76039 late 76038 мөр 76037 ▁Barcel 76037 rát 76034 ▁vuelta 76034 ▁подар 76033 ▁св 76031 ▁sına 76029 ▁Igbo 76028 ▁virš 76028 ▁районында 76028 ▁වෙළ 76026 ▁Шев 76025 ▁Elizabeth 76020 ▁rati 76019 خلي 76018 ▁sveitar 76018 ▁ፀ 76018 даць 76017 199 76015 ▁торлаҡ 76015 ▁makar 76014 ตรวจ 76013 ▁герман 76011 estir 76010 ส่วนตัว 76007 ▁Tár 76005 ▁indik 76004 មុន 76002 ▁ezib 76001 ▁partidos 76001 รอ 76000 ▁bhios 76000 ▁geçti 76000 ደረግ 75999 풍 75999 rova 75998 ▁vegar 75998 ▁obowią 75997 ▁pretek 75995 ▁ნა 75995 wań 75994 ▁iech 75994 ▁doivent 75992 ▁актуаль 75992 alain 75990 ▁спла 75988 ▁სიყვარ 75988 ციპ 75987 ▁რი 75987 urm 75986 ▁apskat 75985 ▁پارٽي 75983 ▁الشع 75982 ▁parfois 75981 ▁мон 75981 ଲରେ 75979 KB 75978 soci 75974 ▁브랜드 75974 ▁реклама 75973 ▁testimoni 75972 ▁باشگاه 75971 olaíocht 75969 லே 75969 कडून 75968 ▁کمیسیون 75967 ▁말씀 75967 ابر 75966 წერა 75966 ልና 75966 EKO 75962 ▁Partido 75961 ▁varmt 75960 搬 75958 กาล 75957 ▁belg 75957 ▁будем 75954 ▁سلط 75954 ▁március 75953 роны 75950 ▁संशोधन 75949 ▁тыры 75948 ▁rehion 75947 ▁alumnado 75941 ▁impression 75941 ចុះ 75939 ▁compromiso 75939 ▁२४ 75939 ▁Правила 75935 kiego 75934 ▁Josh 75934 ▁छोड़ 75933 ▁ତଥା 75933 辞 75931 ítvány 75930 ▁सोशल 75929 եդ 75928 ▁acons 75928 ▁اميد 75928 ▁Categoria 75925 ▁ayud 75925 ▁elegante 75925 ▁pha 75923 ▁දහ 75923 प्रधान 75922 ▁kard 75921 ▁poroč 75921 ▁deport 75918 ພື້ນ 75917 ▁Gant 75915 机关 75915 erencs 75913 供应 75913 ▁Đảng 75912 bət 75911 ▁چھوڑ 75910 ▁Əl 75908 ▁gade 75906 ▁законом 75904 ▁રી 75904 ΛΑ 75902 ▁právo 75902 ▁Тема 75902 attform 75901 ▁gruppe 75901 ▁анга 75901 ▁մատ 75901 ▁النف 75901 ▁komplex 75900 ર્ક 75899 ▁ترسره 75898 ▁wilaya 75897 ▁महान 75897 ្មេង 75896 ประสบการณ์ 75894 ▁गेल्या 75892 ▁CAP 75890 ませ 75890 ▁arbit 75886 ieran 75884 rekin 75884 рава 75883 ဂ် 75882 ▁Koll 75882 ▁Restauranter 75882 ▁الخارجية 75881 ▁drejtat 75880 ▁አድ 75880 אַלי 75879 បន្ទ 75879 ▁rapporter 75878 izam 75877 моги 75877 ▁Γερμαν 75877 izados 75876 に見 75875 ▁Срба 75874 ▁деятельность 75873 ▁കോള 75871 限制 75871 ▁PSOE 75870 ▁hiány 75870 ▁Vera 75868 ിച്ച് 75867 ▁neman 75867 ▁해야 75867 掌握 75867 ▁Коментари 75866 entlich 75864 ▁នៅក្នុង 75863 panh 75862 ريت 75861 ▁راتل 75861 ▁فيصلو 75861 ABB 75859 ▁таке 75858 ▁ozd 75857 ▁степен 75856 ▁نکل 75856 ▁iclas 75855 ▁עור 75855 ▁документы 75854 ▁වෙනත් 75854 ▁Sle 75853 ▁postan 75853 ▁raste 75851 ▁Flam 75850 ▁bliain 75850 ▁double 75847 abaka 75846 ▁सकि 75846 vērt 75844 qituv 75843 ▁وليس 75842 ಕರಣ 75838 dì 75836 ໍານວນ 75832 ▁Saison 75829 ▁lya 75829 ▁आवड 75828 owanym 75826 ▁Sähkö 75826 ▁yoğun 75826 ▁Phần 75825 ▁cilën 75822 ▁azalaki 75821 ोजना 75817 ▁могућ 75817 ▁eriko 75816 ▁fille 75814 ▁ነፃ 75814 ▁GMT 75813 ▁soát 75813 ▁entreprise 75812 ຍາດ 75811 ▁четыре 75809 ▁பெய 75806 ▁kovin 75803 ▁Uqobo 75802 ▁yeux 75802 ▁samy 75801 kins 75800 ▁lescht 75799 ▁голем 75799 ▁vuoksi 75795 ▁φιλ 75795 ▁واقعی 75795 alal 75794 ຈີນ 75794 ▁dae 75794 ▁povz 75794 ▁партии 75794 ▁هتل 75794 ▁لڳو 75793 aboración 75792 ▁niektó 75790 ▁Amor 75789 自我 75788 ▁toata 75787 तर्फ 75786 ▁Markaziy 75785 ▁stanow 75785 ▁היסט 75785 ehr 75784 rektor 75784 Box 75783 ▁επικοινων 75783 insta 75782 ▁tänka 75782 ▁தேர்த 75782 ैर 75781 ▁виконав 75780 tigung 75777 ▁mbe 75772 сана 75771 有点 75770 ▁SHBA 75769 ▁በሕ 75769 ▁4.5 75767 ▁Nicolas 75766 ▁ଶିଶୁ 75766 敦 75766 贡献 75766 ▁misi 75764 maj 75763 ▁nepo 75763 왔다 75763 ▁teknis 75762 ▁ჩამ 75759 ▁cover 75755 ▁formål 75755 slaget 75752 න්ධ 75752 ▁našej 75750 izmu 75748 暑 75748 сва 75747 ▁toimi 75747 三个 75746 ▁JW 75744 ▁giud 75743 ▁tusken 75743 ▁ಮಾರುಕ 75743 appro 75740 środ 75740 ახი 75739 hathe 75738 abba 75737 ▁indien 75737 ▁Аҡ 75735 ▁ପ୍ରସ୍ତୁ 75734 onyesha 75730 ▁inaad 75730 ▁köl 75729 ▁깨 75729 ▁megf 75727 ▁sapi 75727 umaha 75725 対策 75722 adie 75721 킬 75721 ▁አማ 75719 গুল 75718 нові 75717 ▁gä 75717 ▁діяльність 75717 ▁répondre 75716 ▁асууд 75715 ▁تقسیم 75714 ▁küps 75713 ▁skozi 75713 ▁فائ 75713 ▁माध्यम 75712 ▁juures 75706 ▁सचिव 75706 оз 75705 ክተር 75705 ▁atit 75700 ▁១០ 75698 adah 75697 ▁bakom 75696 ▁faritr 75696 ጎች 75695 ▁berapa 75693 ▁kulan 75693 ▁ਯੂਨੀ 75692 技能 75692 ▁formen 75691 ▁საკუთარი 75691 ▁myslím 75688 ▁чиглэл 75687 ▁લાગે 75687 ▁zabaw 75686 ▁прафе 75685 挑战 75685 ▁şöyle 75681 utr 75680 ▁preth 75679 глежда 75678 ▁заболе 75676 ▁simpel 75675 hly 75674 seid 75674 ▁мәлімет 75674 ▁komisija 75671 ▁mundur 75670 ▁chlap 75668 ▁esté 75668 ▁nopeasti 75668 tdi 75667 umit 75666 сных 75666 िश 75666 ▁Johor 75666 ipha 75664 ▁všechno 75663 콘 75663 elektr 75662 ▁голяма 75662 劳 75662 чани 75661 ጠቀም 75661 ▁2.2 75660 ▁விஷ 75658 huelen 75657 тное 75657 ▁148 75656 վարի 75655 させていただ 75655 ungum 75654 éna 75654 حم 75654 မျိုး 75653 ▁rapporto 75651 ▁კულ 75650 ▁mayelana 75648 ▁fasi 75647 ▁gratuito 75647 દર્ 75645 зывается 75644 ▁Masjid 75644 love 75643 iewe 75642 ▁თითქ 75640 ▁چارواکو 75637 შე 75635 ▁Puerto 75635 ▁bestellen 75632 资本 75629 ▁२०० 75628 sken 75626 ▁õpet 75626 тното 75625 ▁품 75625 ▁pequeña 75624 dov 75622 ệm 75622 fulde 75619 ▁државе 75619 ▁Stato 75618 ▁sellele 75618 พุทธ 75617 ▁gefunden 75617 ▁공간 75617 ekaan 75616 ▁formid 75616 ▁ужи 75613 ▁ନିର୍ 75613 ಭಾಗ 75611 ▁сын 75611 ukis 75608 ▁මණ්ඩ 75606 ▁другое 75605 presi 75603 ▁обучения 75602 ▁xər 75601 ▁Όπως 75598 μει 75597 ▁Suche 75597 ডি 75596 რცელ 75596 ▁Instit 75596 ▁fuqar 75596 ალე 75595 ▁පස්සේ 75593 annau 75592 ▁erfahren 75592 ▁jakiś 75592 ▁طبی 75592 ▁થયું 75591 ਕੋ 75590 แสง 75589 ▁Tym 75589 ▁wëllen 75589 กะ 75587 ▁Leute 75587 ющихся 75586 ▁hlj 75586 ▁frais 75585 ▁stavanger 75585 ביל 75584 natt 75583 ▁oczywiście 75583 ▁MOK 75580 ▁чим 75580 사항 75580 oslov 75577 ▁Kı 75577 เช่า 75575 력이 75575 ▁Дей 75574 MER 75573 zési 75571 ▁lương 75571 บับ 75570 ▁kään 75568 igde 75567 មន៍ 75567 ▁(21 75567 дахгүй 75566 ▁análise 75566 ▁Ը 75566 ▁augustus 75564 unang 75562 ▁Magic 75562 ▁معام 75562 ▁mevcut 75560 ▁ορισ 75559 ▁சொல் 75559 ▁dažād 75557 หยุด 75556 ▁शकता 75554 ▁maanden 75553 ▁Hình 75552 ಮ್ಮೆ 75551 ▁presse 75550 ▁yillar 75548 ısında 75546 ▁Cumar 75545 ▁Beispill 75544 hlásiť 75542 ▁людьми 75540 ▁vrát 75539 ▁देते 75539 сымша 75538 ▁trik 75538 ▁Wena 75535 ▁dương 75534 шақ 75533 ▁Ах 75533 ▁utveckling 75530 活用 75528 ▁Esch 75527 费用 75526 ▁Európai 75525 ▁prieži 75525 ▁საათ 75525 ریل 75524 リンク 75523 ▁ເມ 75522 ▁ọma 75522 ▁kindlasti 75521 -/ 75517 ▁доброво 75517 gek 75515 ▁кире 75515 ▁Mob 75514 ▁الشباب 75512 endien 75511 ▁tún 75511 ▁utawi 75511 ▁گوی 75511 ▁buni 75509 ▁রা 75509 attle 75506 ▁življenja 75504 ことです 75502 ▁zehar 75501 ▁Дого 75498 avimas 75494 ▁ಲಿ 75494 ▁අයට 75494 ნდა 75493 ▁sikur 75493 24, 75491 ▁गएको 75491 ston 75490 แลนด์ 75490 ▁zmian 75489 ▁เสื้อ 75487 Օ 75486 ▁autos 75485 ▁kezdő 75485 રની 75484 jep 75482 ୍ରମ 75482 хия 75481 ▁izlož 75481 վեր 75479 אַנג 75478 யில் 75477 ▁Eusko 75474 ▁тисяч 75474 vonal 75473 ▁चाहते 75472 々の 75472 érc 75469 ציי 75468 ▁poin 75468 ▁piše 75467 сән 75463 ▁Cool 75463 ▁eka 75463 ▁эффек 75462 ▁cash 75461 ▁tiempos 75461 ▁בזמן 75461 ▁снимки 75460 ▁HEL 75459 որոշ 75456 ▁repas 75453 ▁svého 75453 ▁зру 75453 ▁történet 75452 ▁njik 75451 ▁ইম 75449 ▁แต 75448 ängen 75447 ყურ 75446 ▁nokus 75446 ხარ 75445 ▁प्रहरीले 75444 ▁taot 75442 ▁ibarət 75441 离开 75441 ▁ਸਾਬ 75440 ▁inova 75439 ▁maraq 75438 ствии 75437 верса 75435 ▁imao 75433 ▁článku 75433 ▁✔ 75433 ▁станет 75431 РГ 75430 ▁regard 75429 农业 75429 ▁bayat 75428 ▁ае 75426 ファイル 75426 forhold 75424 miques 75424 សម្រ 75424 ▁آسیب 75424 大部分 75423 ▁demokr 75422 ▁çözüm 75422 ▁Хеҙмәт 75422 lerimiz 75420 ▁fhios 75420 ísticas 75419 ▁Ок 75419 ▁ଯୋଜନା 75419 как 75418 ▁acel 75418 هير 75417 ภัณฑ์ 75417 ▁بدان 75415 ▁infin 75414 ▁зураг 75411 เข้าร 75409 ▁lewer 75407 ▁الحا 75407 גו 75406 ▁стану 75405 റിന്റെ 75404 ▁137 75404 ▁რეჟ 75404 ▁hizi 75403 ▁sole 75401 ▁બહુ 75401 ,19 75400 ▁राय 75398 ▁propre 75396 ▁Alisema 75393 [4]. 75392 ▁menentukan 75392 лять 75391 ▁sangue 75391 ▁weken 75389 ▁دوباره 75388 ▁каких 75387 촉 75387 escola 75386 ▁samostat 75385 ições 75384 ▁пути 75384 ▁ekst 75382 ▁šol 75380 ásokat 75378 ട്ടുള്ള 75377 ▁расши 75376 ▁꿈 75374 刺激 75374 gget 75373 əzi 75373 ▁संगठन 75373 ស៊ី 75372 ▁ترڅ 75372 store 75371 钢 75371 ▁foydalanish 75368 ▁sinó 75368 ▁नया 75368 irwa 75366 ▁Bant 75363 ▁dereitos 75363 ▁šodien 75363 עיף 75362 цэг 75360 ▁atten 75360 achten 75359 ▁Nhiều 75358 ▁obu 75356 viešu 75354 ಟಕ 75353 ▁5.0 75351 ▁מיני 75351 ຸ້ມ 75350 okuv 75349 ▁عقل 75349 ▁односи 75347 ▁הרצ 75347 ▁ընտանի 75346 ॉल 75343 ersp 75342 ▁eurot 75342 κοπ 75340 整体 75338 legget 75337 сника 75336 ▁Вол 75335 ▁დამატ 75335 apel 75334 ▁изнена 75334 ▁religion 75332 ▁bertemu 75331 ▁日本語 75329 ▁ಮಾತು 75328 ▁tost 75326 പ്പോഴ 75325 ▁Оның 75324 ▁әҫәр 75324 ▁ሌ 75324 ▁bērniem 75323 ▁dezak 75322 ▁zostanie 75322 ▁győ 75321 ▁автомобили 75320 ▁2014- 75318 ▁wej 75318 PU 75317 ▁ejer 75317 ▁intentar 75316 skimi 75314 ហូត 75314 ▁sindic 75312 ▁સામ 75312 ▁zalog 75311 ▁вай 75311 ▁herunder 75310 ▁Pagina 75307 ▁zinto 75306 ▁tepër 75304 ▁tanım 75302 ▁najważ 75301 ▁perque 75301 ▁vigtigt 75301 ujúci 75300 ▁peli 75300 の上 75299 genhet 75298 сады 75298 ▁construir 75298 ▁файҙаланған 75298 ▁Fax 75296 ▁composto 75296 ▁nega 75296 ▁менш 75296 難しい 75296 ungsb 75294 ຊົນ 75293 ▁podzi 75293 телството 75291 ▁fw 75290 ▁env 75288 ▁появля 75288 ▁ଏଭଳି 75286 ▁ранее 75285 ▁money 75283 ▁ensiklopedia 75282 rasta 75279 ▁نیروهای 75273 ▁kultūros 75272 ▁weinig 75272 ▁сәғәт 75272 малко 75271 ▁Disco 75270 ▁motoc 75269 ▁megny 75266 ▁miesi 75265 olek 75261 ▁سکتی 75261 ▁alias 75260 ▁سڏ 75260 ▁ডিসেম্বর 75259 емый 75257 ▁Biy 75257 eset 75256 ഗ്രഹ 75256 ▁Mier 75255 энне 75252 ▁Estas 75250 ▁ਕਿਸਾਨ 75250 ▁Museu 75247 ▁არჩევ 75246 균 75246 ▁Ademais 75244 ▁aprilie 75243 ▁ច្បាប់ 75241 ▁пријате 75240 ▁پسر 75240 ▁Break 75238 कल्प 75237 ▁100. 75237 ▁Moss 75236 ▁დაბრუნ 75236 ▁паза 75235 ▁ቡድን 75235 ▁Atom 75234 iligen 75232 ▁simil 75232 ▁кім 75232 ▁demasiado 75231 ▁എഴുത 75227 lilik 75226 θηση 75224 ▁saged 75223 ▁subpanid 75223 ▁Höhe 75221 ▁gela 75221 commerce 75218 नगर 75217 십시오 75217 ▁clicatz 75216 ugainn 75215 ▁koste 75213 ▁tå 75212 ▁juna 75211 ▁tekij 75211 ▁liyane 75209 ▁خیابان 75209 ▁посмотреть 75208 ▁ചൂ 75207 ▁odstav 75206 ▁কাছে 75205 klan 75202 ▁privada 75201 élyes 75200 ଟକ 75200 ▁ארי 75200 ▁ඡන්ද 75199 ítom 75198 ▁للج 75194 neď 75193 fig 75192 mals 75189 ▁блі 75188 ▁아주 75188 ▁শী 75186 ▁>>> 75185 ▁ترامپ 75184 աստան 75182 miseks 75179 ▁سپریم 75178 ▁daglig 75177 ทะเบียน 75176 ▁проя 75175 ▁პრეზიდ 75174 qembu 75173 ▁okazji 75172 ▁Jeung 75171 ▁annadas 75171 ▁пів 75171 ▁Sər 75170 egli 75169 ▁tahaka 75169 ▁votos 75168 træ 75167 ▁zif 75165 ține 75164 ▁Украине 75163 maður 75160 ▁omunye 75160 ▁реч 75160 ▁ግንባ 75160 ▁Bux 75159 ▁Revista 75159 ประชา 75158 ▁Airson 75157 ▁Ambas 75156 ▁lojë 75156 ▁понад 75156 ▁gyakorlat 75153 我也 75151 配合 75151 ▁બહાર 75150 ▁Mərkəz 75146 ▁brend 75146 ▁целью 75146 ▁Niet 75145 شهر 75144 ▁esperien 75144 ▁uliopita 75142 κοι 75141 ▁أغسطس 75141 ▁Urang 75139 ▁хэмээн 75139 ಶಿ 75137 ▁ведом 75137 ⸻ 75135 角色 75135 ▁Struktur 75134 ▁pandangan 75134 ▁припре 75134 ▁прои 75134 лями 75132 ▁Vita 75132 원의 75132 ▁Eco 75131 ਬਰਾਂ 75130 ▁புத்த 75130 ▁szempont 75129 ▁saglab 75128 ▁жените 75128 べて 75128 ▁POP 75127 ▁სად 75127 ▁ਅਸੀਂ 75126 ▁среща 75123 ▁مراجعه 75123 ▁જૂ 75123 ▁بلو 75122 ▁nejlepší 75121 ▁decision 75120 ▁rusk 75119 ▁ହଜାର 75119 ▁anz 75118 ▁dormir 75117 öiden 75116 ▁לכת 75116 ▁እንኳን 75115 ▁Dragon 75112 ▁dalawang 75112 ickt 75111 ▁پیشنهاد 75111 örtén 75110 ცირ 75109 ▁toes 75109 ▁Então 75106 ੋਰਟ 75105 fih 75103 ▁toer 75103 ▁Macron 75101 ▁meningkat 75101 ▁круг 75101 biag 75100 ▁форума 75098 ландия 75096 ▁Русије 75096 ▁модер 75095 ▁તેમની 75095 idega 75093 ▁έργο 75092 omě 75090 ▁우리가 75088 vänd 75087 laq 75086 цела 75086 ▁һан 75086 ▁क्षमता 75086 ▁τού 75085 ▁zajedno 75084 ajuan 75083 ▁υγε 75083 ▁Slim 75082 ▁ឃុំ 75082 ▁only 75080 사진 75080 рге 75078 комму 75077 ▁Дона 75077 ▁mañá 75076 ▁মেয়ে 75075 ▁ცხოვრება 75073 ▁kvalifika 75072 ▁ഫ്ര 75072 тість 75070 ਰਾਮ 75069 ▁telepon 75069 案件 75066 meden 75064 ▁mohla 75064 ▁ນາງ 75063 ▁quên 75062 ▁requer 75061 ▁નવ 75061 olakala 75059 ▁László 75059 ▁ngũ 75059 ▁vời 75059 ▁глаза 75057 ▁запозна 75057 ▁қоғамдық 75056 ▁ಸುದ್ದಿಗಳು 75056 簡 75056 ημα 75055 ▁व्यक्ती 75052 ▁fejlő 75051 ወቅ 75048 ẹp 75048 ▁നോക്കി 75048 ▁गाउँ 75047 ▁밝 75046 ▁депози 75045 اا 75044 ▁Prabu 75044 ▁شڪار 75043 ▁ileti 75042 ▁គួរ 75042 אַלט 75040 ▁наведе 75040 फे 75039 ▁Technolog 75039 spolitik 75038 ▁اليمن 75038 ettings 75035 ▁arre 75033 ▁مقدس 75033 ▁maailm 75031 ▁selg 75031 สต 75027 ▁карт 75027 ▁dant 75026 ▁සභා 75026 زمین 75025 ▁granda 75025 ▁καλο 75025 (3 75024 ativen 75022 ▁2-1 75021 ▁valmi 75021 ▁آخری 75020 ▁භාව 75018 ▁apay 75016 ξο 75015 ریشن 75014 ▁बाबा 75013 ▁נוצ 75011 金額 75009 bita 75007 ▁이루 75007 ovine 75005 ▁пә 75005 ▁katastro 75003 ▁औष 75003 intahan 75002 ▁Skor 75002 .0% 75001 siu 75001 ▁ဆက္ 75001 ස්සෙ 74999 ▁namoota 74999 ▁vanligt 74999 ▁MG 74998 ▁mogao 74998 ▁영향 74997 各種 74997 ▁بابا 74996 ▁njerëzit 74993 ▁pierwsze 74993 ніше 74992 ▁аспе 74992 ▁CAT 74991 ůst 74990 ▁beig 74989 ▁lass 74989 真是 74989 ▁Dè 74988 ▁გზ 74988 ▁158 74987 ▁Ķ 74986 ▁Σύμφωνα 74985 ▁ڏک 74985 ▁ROM 74984 ▁אחרת 74984 ▁configura 74983 ▁встреча 74983 ឱ 74982 ▁assum 74981 ▁дүн 74981 ▁आन्दोलन 74981 ▁ஜெய 74981 ▁ฉัน 74981 疫 74981 diu 74979 ▁näischt 74979 こそ 74979 ▁અં 74976 uthuk 74966 ▁Juma 74966 ▁kedvez 74965 க்கூ 74962 әүе 74960 झे 74959 đuje 74958 ଦ୍ୟ 74958 ▁allanol 74958 ▁تخت 74958 ▁ກອງ 74958 ▁context 74956 armente 74955 ▁gih 74955 ▁tipp 74955 બો 74954 ▁Шул 74953 ▁හු 74953 ▁barne 74951 েনে 74949 न्दर 74948 ▁küsim 74943 ופים 74942 ▁ερω 74941 ▁lina 74940 ▁nouveaux 74940 ▁oif 74940 ▁pelig 74939 ▁вашите 74938 awal 74935 ▁χαρακτηρισ 74935 ▁बीजेपी 74935 屏 74935 ▁helposti 74933 vezet 74931 ▁Əgər 74931 ▁zip 74930 otsh 74929 веты 74929 ▁tilante 74928 ▁zic 74925 ▁pasaulē 74924 ▁Страте 74924 maksas 74923 ▁गणेश 74923 ▁bain 74922 नै 74921 ashtirish 74918 τισμού 74917 dure 74913 ▁verksamhet 74913 ▁бүрд 74913 azienda 74912 कुमार 74910 ▁Trondheim 74909 ▁lõpet 74909 emde 74908 ▁участва 74908 зіі 74907 ▁berwarna 74904 ▁įs 74904 элтэй 74902 mint 74901 ▁саны 74901 ктери 74900 ▁наистина 74900 ▁ເມື່ອ 74900 තියක් 74899 ▁podmien 74899 urger 74898 が多い 74898 қын 74896 ▁permette 74896 ▁skrift 74895 raucht 74893 фектив 74893 ବ୍ 74893 ▁käyttöön 74893 minu 74891 okite 74891 誉 74890 yda 74888 ایم 74886 ▁боя 74886 ोष 74883 ▁gyr 74883 ▁ағза 74883 ▁dimost 74881 ▁سري 74881 μης 74880 reib 74879 ətta 74878 ▁tük 74877 rifysgol 74876 ▁хал 74876 ▁बुद्ध 74876 ▁етә 74875 Char 74874 лац 74874 ▁намере 74873 依然 74873 多个 74873 ▁Føl 74871 ▁dalyva 74871 現場 74871 ustar 74870 ინგ 74870 ▁ບ້ານ 74869 utsch 74866 ▁Unser 74866 内部 74866 plassen 74865 ▁башҡары 74864 ▁سبز 74864 ebben 74863 ▁Олон 74863 虎 74863 ▁कायम 74862 මී 74861 哪些 74861 inian 74860 ▁صفح 74859 Ту 74857 願い 74855 ▁fogyaszt 74853 ątek 74852 ▁nustat 74850 ▁אות 74848 ▁נסי 74848 ▁snim 74847 ▁activités 74846 alnych 74845 ገረ 74845 ▁Rú 74845 なぜ 74845 ให้น 74844 ▁dytë 74841 êtes 74838 ▁priz 74838 ▁(- 74837 folyam 74836 носта 74836 ▁иногда 74834 ▁مستقبل 74834 ▁ülke 74832 ▁Bó 74829 ▁libur 74829 ▁meillä 74829 ▁parking 74829 etarako 74828 ্লে 74828 fhe 74827 ▁Grouss 74827 ▁mindent 74827 ▁lortu 74826 ▁selanjutnya 74826 ▁tắc 74826 突破 74826 ▁Kids 74825 ▁уҙ 74825 ītas 74824 ▁nogomet 74823 ▁ପର୍ 74823 ▁Vs 74819 icho 74818 ▁порушення 74817 赞 74817 видно 74815 ისთვის 74815 ▁мас 74815 ক্তি 74814 ▁чаще 74814 ▁abr 74812 тәрендә 74811 ▁urząd 74811 ▁윤 74811 ավորված 74810 ▁Quel 74810 认真 74810 ▁уво 74809 ▁متفاوت 74809 брати 74807 ندہ 74804 ▁Məlumat 74804 ▁төлөө 74804 ▁மறு 74802 िकल 74801 ▁здоровья 74801 ▁पढ़ें 74801 éisst 74800 ▁terpaksa 74800 ▁Serra 74799 ควบคุม 74797 ▁malas 74797 obiť 74796 ▁Ezek 74795 ▁Moon 74795 ინისტრ 74794 ▁sprij 74794 ▁ΚΑ 74794 ▁نتایج 74793 伝え 74793 ျပင္ 74792 ненне 74790 ənclər 74789 ▁každého 74788 ▁yor 74787 क्रम 74785 ▁וכן 74781 sein 74780 haraha 74779 ▁strip 74779 ләк 74778 ▁kuning 74777 τροπ 74776 แพง 74775 တမ္း 74775 ▁tron 74775 ▁പ്രതിക 74775 sier 74774 ناہ 74774 ▁besoins 74774 ▁artis 74773 ▁sæl 74772 文明 74772 ▁mellé 74770 ēto 74767 ▁ಊ 74767 ▁Bož 74765 ▁isugu 74765 iejs 74764 ▁preuz 74764 lili 74762 യിരുന്നു 74762 ▁cig 74762 niegt 74761 ▁חיפוש 74761 utate 74760 κύ 74760 ंडी 74758 risto 74757 ▁Menn 74756 ණයක් 74755 ▁caafima 74755 ▁sofr 74755 กิ 74753 ▁răspuns 74749 dent 74748 ▁pandan 74748 izacija 74746 ▁Daghan 74745 ▁Schreiwen 74745 ▁(1, 74744 ▁Emb 74744 ▁Umno 74744 נסט 74743 ทรัพย์ 74743 ▁रण 74743 偷 74743 ▁အိမ္ 74742 Kan 74741 ▁legitim 74741 公里 74741 ▁אַזוי 74740 əyən 74739 ▁بیشتری 74738 ▁পর্যন্ত 74738 ettävä 74737 ▁Laba 74736 ffes 74735 ubuka 74735 ▁Felhasznál 74735 افية 74733 ▁aquelas 74733 ▁Στη 74733 ▁содер 74732 ▁shkoll 74731 нету 74730 વાથી 74730 ფორმ 74730 ▁puoi 74730 ▁NOR 74729 ▁kinap 74729 ▁Hercegovina 74728 ftung 74726 бэр 74725 ▁skelb 74725 ▁غز 74725 čiaus 74724 ▁Isla 74724 ▁varie 74723 ▁ಏನ 74723 ▁mtoto 74722 ▁странице 74722 ▁टक् 74722 ▁Lakini 74720 ▁قادر 74720 ▁اتاق 74719 ▁Микола 74718 ▁sredstva 74717 ▁sunk 74716 ▁يف 74716 ▁Hüseyn 74713 ▁сообщи 74713 变得 74713 ▁oude 74712 鐘 74712 unq 74710 άνω 74710 ▁जनाको 74709 yerek 74708 ▁Moder 74708 エン 74708 ▁BAT 74707 سین 74705 悪い 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skin 74333 ໂຕ 74333 ▁1933 74333 ▁zawar 74333 ebes 74332 ▁μεγά 74332 ▁марки 74332 ▁Parlimen 74331 ▁folkl 74331 半年 74330 emang 74329 广东 74329 ▁terbesar 74328 ▁티 74325 ▁በሁ 74323 ▁Fedde 74322 ເສດ 74321 ▁baki 74321 ▁działalności 74318 ▁avril 74315 ▁Evropi 74313 asam 74312 သီး 74312 ▁entanto 74312 ▁jääb 74311 ▁аудан 74310 ▁координа 74310 eleid 74309 ▁Oi 74308 bys 74307 tiene 74307 зінде 74307 ▁व्यवसाय 74307 ▁நம்ப 74307 美容 74307 ▁quieres 74306 wych 74304 ▁kaikille 74304 ▁война 74303 ▁ludzie 74301 ▁Etx 74300 ▁पैसे 74298 ▁संबंध 74297 ▁تقدم 74295 ▁світо 74293 ெல்லாம் 74292 aixo 74289 gylch 74288 ▁joiden 74288 ansin 74287 ▁Corporation 74287 ▁Nchi 74286 ▁kani 74286 ▁Deniz 74285 ▁Прэ 74284 jelent 74283 ▁башинформ 74282 ▁מיוח 74282 ▁σημει 74280 entic 74279 ▁ႏွ 74278 ▁ԱԺ 74277 ileen 74276 ▁дай 74275 ▁ಕೊಡ 74275 دية 74274 sachadh 74273 ▁колька 74271 ▁juillet 74270 ▁setzt 74268 ▁شناسی 74268 ▁kād 74267 ▁دیر 74267 ▁ਸ਼ਾਮ 74267 ▁አባላት 74266 uphela 74263 ▁enkl 74263 ▁મેં 74262 hia 74258 ▁hverdag 74258 ▁Khách 74257 ungelo 74256 ▁Наша 74256 척 74256 ▁cami 74253 ▁gascons 74253 demokrat 74252 ▁गयी 74252 ▁тохиол 74248 сама 74247 ▁Bö 74247 ▁கேட்ட 74242 ੁੱਖ 74239 ▁érdemes 74239 ▁όλων 74239 ÄR 74238 ▁pagarigan 74238 ▁திற 74237 obin 74236 ▁Cynulliad 74236 ▁nécessaire 74236 ▁кожна 74235 私が 74235 ▁Boston 74233 ▁climat 74233 ▁osobních 74233 matig 74232 ▁וועל 74232 ालय 74231 числен 74230 ्ला 74230 ▁orthu 74230 ▁Consell 74229 ▁gars 74228 ▁מחיר 74228 uhlungu 74227 ▁хоть 74227 ▁scher 74226 ▁свята 74225 félag 74224 ▁Yusuf 74224 ▁vòl 74224 ▁организација 74224 ▁အစ 74224 nana 74223 ცხოვ 74223 ▁эз 74222 ობდა 74221 ▁bidh 74221 最終 74221 மதி 74219 ▁гісторы 74219 ▁Nupay 74218 tset 74216 ▁საუბ 74216 ▁ixt 74215 lihat 74213 ▁معد 74211 ▁šiuo 74210 missioun 74209 लों 74207 ೋಜ 74207 ▁nakakhulu 74207 osim 74204 ▁รูป 74200 ▁මාර 74196 ▁Waxay 74195 ▁долларов 74195 ▁kawo 74193 szt 74191 ▁gg 74190 ▁prispev 74190 мының 74189 ▁Divers 74189 ▁hut 74184 ▁kërko 74184 ėte 74183 ▁caps 74181 涉 74181 ▁случај 74180 дзены 74179 दू 74179 ▁Najbol 74177 Ser 74176 utis 74176 visor 74176 ▁επο 74176 会員 74176 ningsm 74174 ▁neamh 74174 รู้สึก 74172 ubula 74171 jonale 74169 ▁konuda 74169 ▁хугацаанд 74167 脱毛 74166 ▁Taiwan 74163 ▁kasutat 74163 ▁voksen 74163 òta 74162 ▁ગે 74162 ▁přímo 74161 ▁хороо 74161 หนุ่ม 74157 ▁negy 74157 ▁egentligen 74156 ਸਪ 74152 ដើម្បី 74152 ▁interno 74152 ▁letech 74152 ஸ்ட் 74151 10.2018 74149 ljivi 74147 ▁поможет 74146 ▁الإسلامية 74146 כנולוגי 74145 ատվական 74144 ▁Campus 74144 ▁frou 74142 ссор 74141 ▁տեխն 74141 하거나 74141 ▁इसका 74140 ▁heescht 74139 홍 74139 ▁Základ 74137 ▁տալ 74136 ▁muncă 74135 ▁müavini 74135 МЕН 74134 ▁தன் 74133 gaam 74131 ▁Imm 74130 ▁MAH 74127 ▁nachádza 74126 ▁neien 74125 ▁Sonra 74123 ▁esing 74122 ▁gréasáin 74121 ിക്കു 74120 ▁ಅರ್ 74120 erimis 74119 lač 74119 දීම 74119 ▁román 74119 laden 74118 40, 74116 ▁питање 74116 ▁Schne 74114 ▁Уче 74114 仔 74114 ▁funkcion 74113 ▁handle 74113 ▁сих 74113 ארק 74112 ▁bâ 74111 ▁gói 74111 oju 74109 ▁cewar 74109 ▁Lehen 74104 ▁уңай 74104 ▁ကုိ 74103 əst 74102 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րանս 74026 ▁ponte 74024 ្ពស់ 74023 ▁Kiedy 74023 ▁הראשונה 74023 ▁utanför 74022 ▁wythnos 74022 ▁олар 74021 amano 74019 ▁kosti 74019 ന്മ 74017 aktion 74014 ▁centres 74014 щер 74012 ▁Donn 74012 ▁KER 74012 ▁mily 74011 ▁oir 74010 perio 74009 ▁helg 74009 ▁ኃላፊ 74007 ▁Ferd 74006 ▁rigu 74006 ▁Хай 74006 ▁Gruppe 74005 效率 74004 ▁atzī 74001 ▁Expl 74000 ▁видов 74000 移动 73999 雲 73999 helwa 73998 šinou 73997 ▁Τέ 73995 otur 73994 ▁गठ 73993 ιασ 73992 ▁defend 73991 ▁hạt 73989 ▁ritmo 73989 ▁166 73988 ▁வாழ் 73987 luğ 73986 ▁гәзите 73986 확 73982 ▁Clara 73980 せる 73978 ▁alguém 73976 ନରେ 73973 ണ്ണി 73973 กระจ 73973 ווח 73972 ▁периода 73972 ▁hae 73969 ▁plantes 73969 ▁اینترنت 73968 สะอาด 73967 empilo 73965 であれば 73965 ▁хеҙмәте 73964 ▁બિ 73964 szág 73962 ▁pridržane 73961 ிகளை 73959 ▁nete 73959 ▁воду 73957 縣 73957 ियाँ 73956 ဌာန 73956 ▁Sidoo 73955 ējam 73953 ▁Bella 73952 ▁estudos 73951 ▁२०७ 73951 ▁ההת 73949 ტარ 73948 ▁Basis 73948 ▁gabi 73947 ▁აქტი 73943 Mart 73941 ▁enhliziyweni 73941 novi 73940 lji 73939 ▁самостоятельно 73939 ▁연결 73937 ▁Ся 73936 ▁Abrëll 73935 unikeza 73934 וער 73934 ANGO 73933 őbb 73933 ▁polítiques 73929 τρά 73928 нэт 73928 ▁truk 73928 ▁Won 73927 ▁መሆኑ 73924 ຂໍ້ 73922 ▁termo 73922 ▁Дже 73921 ▁लें 73921 ▁considerar 73920 ▁napan 73918 ▁këtu 73917 ▁клуба 73917 žbe 73916 ▁း 73916 Wir 73915 ▁Πό 73915 ▁پام 73915 ▁associa 73914 ▁onem 73914 ▁дожи 73914 ▁କାର୍ 73914 ▁എല്ലാവ 73914 ▁برگ 73913 qaalaha 73912 ▁наба 73912 версите 73911 ▁समु 73911 pac 73910 ▁Rup 73909 ▁대부분 73909 ▁граѓаните 73908 ▁توافق 73908 uphatha 73907 כס 73907 、3 73907 íka 73906 ▁предизвика 73906 ಟು 73904 漂 73904 Gi 73903 ଷ୍କ 73902 ▁endale 73902 ▁istitu 73901 ▁gebracht 73900 ▁خرداد 73899 ರ್ಚ 73898 ▁fama 73898 შვილის 73897 ▁ajung 73895 ▁hozzászól 73895 コメント 73895 វិធី 73894 ▁धन्यवाद 73894 കട 73893 ▁Яна 73893 ▁вашего 73891 ▁ေဖ 73891 fton 73890 することができます 73890 ▁Xəbər 73889 هود 73888 ▁الاعت 73888 と思った 73888 ▁кілько 73886 ികളെ 73885 ▁tietoa 73885 ▁təsir 73885 ିଲି 73884 nà 73883 ▁quals 73883 ▁étaient 73883 ▁એટ 73883 بود 73882 ▁näht 73881 ▁әкімі 73881 ▁opět 73879 ▁segundu 73879 ▁પડી 73879 ništva 73876 ▁মাধ্যমে 73876 ផ្ទះ 73875 ▁jeigu 73875 ▁Maendeleo 73874 ▁Им 73874 ▁windows 73872 eksu 73869 ▁abban 73869 ăci 73868 ▁hjemmes 73868 ▁поже 73868 ▁наблюда 73865 ussy 73863 ▁2021 73859 ▁fazendo 73857 ▁إنه 73857 ▁Geç 73855 다운 73855 ▁ଟଙ୍କ 73854 ເຫດ 73852 ▁Serb 73851 ▁geral 73849 ▁толку 73849 зія 73847 良く 73847 ▁közös 73846 hachd 73844 ▁Kenapa 73844 lear 73843 ▁awam 73842 ▁fann 73842 ngol 73839 ▁tS 73839 ▁маань 73839 ▁میلی 73839 ▁লক্ষ 73839 ▁һайын 73837 र्चा 73833 ▁galt 73833 ▁wës 73831 ▁ຕິດຕໍ່ພວກເຮົາ 73830 ▁matery 73825 ▁manga 73824 ▁yerinə 73821 ▁сапраў 73820 ▁mengatasi 73819 ▁clave 73818 ensie 73817 ittäm 73817 ▁ନିଜର 73815 ▁زړه 73814 ▁írt 73810 akuk 73809 ▁mast 73809 困难 73809 ▁reizi 73806 ▁Бро 73806 ▁cilvēkiem 73805 illeurs 73804 пти 73804 ยาม 73804 ▁poslova 73803 ▁گروپ 73803 ▁기간 73803 ▁haciendo 73802 ▁fremtid 73801 ▁friv 73801 ▁Sayt 73799 ことができる 73799 gut 73798 ▁Šv 73798 ▁blad 73795 ▁аҙа 73795 ▁зрения 73795 yyttä 73794 ▁Pangeran 73794 ▁bande 73794 ▁vuosi 73794 Dis 73793 ▁helgen 73792 ▁Comis 73791 ▁pasiek 73790 庆 73790 დეთ 73786 ▁месту 73785 ▁гер 73783 ▁utvalg 73782 ▁йәштәр 73782 ▁የጥ 73782 ză 73777 rosa 73776 ียร 73776 ▁artikler 73774 ▁dienst 73773 ▁ואני 73772 enwe 73771 ▁សង្គម 73771 ▁εργασ 73770 vita 73769 ▁Glück 73769 เรียบ 73768 ബ്ര 73767 ▁Muito 73767 ▁aduce 73767 ▁candidato 73767 ▁persze 73767 ▁तुम्हाला 73767 ግለ 73765 ▁асоб 73765 ▁قصد 73765 ▁आम् 73762 ▁überhaupt 73760 ▁şart 73760 ▁niistä 73759 ▁remaja 73759 ໍາລ 73758 ▁қабі 73755 ▁mjeseca 73754 ▁(2000 73753 irenena 73751 ▁투자 73751 íp 73750 мум 73750 ▁ANC 73750 ▁ekl 73749 ▁şik 73748 ▁vietos 73746 لیل 73745 ▁Alat 73745 ▁конкурсы 73745 ▁ምክንያ 73744 hlon 73743 ▁придется 73739 ▁ਕੌਰ 73739 ▁TÁ 73738 ▁müd 73738 ▁ikas 73737 ▁temi 73737 בול 73736 ▁tids 73736 ▁ресто 73734 /20 73733 ▁Tus 73733 ▁deten 73733 ▁فروشگاه 73731 ;; 73730 ariş 73730 olch 73728 לון 73727 ▁utenti 73726 ilised 73724 的には 73724 itais 73723 ליח 73723 ინისტ 73723 ▁പോലീസ് 73723 ▁රියාත්මක 73722 ▁중요한 73722 ていて 73721 ultimo 73720 መት 73719 ▁enlla 73719 isb 73716 ▁daou 73716 ▁izith 73715 ▁increment 73714 ੈਕਸ 73713 ினர் 73711 ▁importants 73709 geber 73708 терә 73708 class 73705 ▁ocaz 73705 证明 73704 選手 73704 partuat 73703 ▁Наш 73703 wemo 73700 ▁የሌ 73700 ሰቦች 73699 lacions 73698 frei 73697 ▁Micha 73697 ▁المقد 73696 ٤ 73694 ▁کری 73694 icne 73693 ▁Teatr 73693 ▁zakres 73688 itània 73687 ▁Nə 73686 ▁ხდება 73685 ▁(2017) 73684 ▁Ел 73684 ▁Uvjeti 73683 isessä 73682 自分で 73682 ләнде 73681 ▁FEL 73681 做的 73681 intzen 73678 ▁tarny 73678 AME 73677 ▁avond 73677 प्प 73675 ജ്ഞ 73673 )))) 73671 ▁якої 73671 рую 73670 ▁võivad 73670 윤 73668 onnan 73667 ▁оцени 73667 ▁تؤ 73667 angt 73666 ismi 73666 әмәт 73666 שון 73664 forder 73663 adiisa 73661 ▁toată 73659 ▁służ 73658 ▁Fav 73657 ▁Makanan 73657 ▁valaki 73656 ennem 73654 φί 73654 还要 73653 ▁Agung 73651 orrido 73650 ായിരുന്ന 73649 ▁şəbək 73649 iecte 73647 ▁الحل 73646 ▁kafe 73644 ▁llau 73642 ▁każdej 73641 ▁manc 73640 рыбы 73638 ▁حفاظت 73637 שק 73634 ▁zvyš 73634 ▁ଶିକ୍ଷ 73633 ▁തുടര് 73632 ▁ersch 73631 ▁гривень 73631 ▁орчин 73631 ▁বিএনপি 73631 روب 73630 ▁kiel 73630 탕 73630 kulunkulu 73629 ▁мэнд 73629 ▁тикшерелгән 73629 ▁metre 73628 saak 73626 ▁MGA 73625 ▁proporcion 73625 ▁ukon 73625 laterra 73624 ▁অনুষ্ঠিত 73623 ▁tutv 73621 ▁भन्नु 73621 ▁powietr 73618 ▁먼저 73618 ▁Миха 73617 zęd 73613 ülete 73613 ▁podrí 73613 ագիտական 73612 ▁കുറ്റ 73611 ▁спри 73610 ▁סדר 73609 中で 73609 ▁gerekti 73607 ▁mawr 73607 ▁одному 73607 ▁หลังจาก 73606 ▁играта 73605 ▁സ്വീക 73605 ಿತಿ 73604 ▁сери 73603 woh 73601 inza 73600 ▁اکثر 73600 ESİ 73598 ▁Lue 73594 스토 73594 ▁Jenis 73593 ▁allora 73593 ▁کی۔ 73592 ▁szczególnie 73590 ▁הבא 73590 ▁વાર 73590 ဆန္း 73589 vani 73588 udak 73587 indleko 73586 ▁samarbeid 73586 ▁aglaon 73585 лица 73583 ▁jarang 73582 ▁powinien 73580 ▁naveden 73579 orsanız 73578 인트 73578 ፌዴ 73576 Top 73575 ▁Voll 73575 하였다 73575 нув 73574 地球 73574 ดื่ม 73573 ▁наука 73573 ▁مفید 73573 ▁Geen 73570 ▁unhas 73570 मर 73569 ▁Universitas 73568 ▁дапамо 73568 äsident 73567 ereo 73566 ▁Lw 73566 ▁yanada 73566 ▁chip 73564 ▁1923 73563 ▁tampil 73561 ▁acced 73560 ▁يكن 73556 geh 73553 ▁сістэ 73551 下降 73551 ▁chụp 73550 ▁piedāvā 73550 ▁amerikai 73549 aramat 73547 ▁delikat 73546 جاح 73544 ਤਾਬ 73542 päev 73541 ▁පැත්ත 73540 fanele 73539 łeś 73539 ▁bekym 73539 ▁jõud 73539 ▁тавь 73538 ztem 73536 ▁ihtiya 73536 ାତ୍ରା 73535 ▁cầm 73535 рее 73533 ▁יחד 73533 chain 73531 ▁ببین 73531 ▁tales 73530 alanan 73528 hansi 73525 ייני 73525 ımızı 73522 תרון 73522 ▁Lors 73521 書き 73521 人物 73520 었던 73520 ▁қан 73519 mür 73518 टेड 73518 ▁للش 73518 ▁중요 73518 gisayar 73517 jedn 73517 udun 73517 биле 73517 ▁taq 73517 fikia 73514 ▁действие 73514 ▁tillgäng 73513 ▁Монго 73513 ▁अत्य 73513 ოდეს 73512 ▁vähemalt 73512 ▁болу 73510 ▁ដែរ 73510 tidas 73508 语言 73508 ▁buduć 73506 ▁společnost 73504 ▁consolid 73502 ▁savaş 73502 ▁Рәсәйҙә 73499 ▁რაღაც 73499 ƴƴ 73498 ▁honlap 73498 ▁வாழ 73498 iveau 73497 brand 73495 háil 73494 ▁Ё 73494 ڏھن 73493 ▁Atatürk 73493 ווק 73492 ▁desaf 73490 ▁שוק 73490 ▁juttu 73485 ţiilor 73484 ЖЕ 73483 արակ 73483 ▁знания 73482 ළුව 73480 ▁koum 73480 ▁माम 73480 ▁daļa 73478 ètres 73477 ▁مسی 73477 说明 73477 هایی 73473 ▁podetz 73473 ▁ҡаты 73473 möglich 73472 vano 73472 ▁1934 73471 ▁cocina 73471 ▁projekto 73469 įž 73468 လုိက္ 73468 ▁activa 73466 ▁ਸਪ 73465 ulua 73463 ▁ciągu 73463 Informa 73460 arbeiten 73460 ▁Outlook 73459 ▁башлап 73459 ▁Kurulu 73458 бів 73457 ▁عبارت 73456 ▁baf 73455 ▁spektak 73455 ▁लै 73454 דיע 73452 ▁Γα 73450 ▁สถานที่ 73450 ▁NAT 73449 tañ 73447 ▁робити 73446 ▁травня 73444 ்ப்ப 73443 ▁عليك 73443 名字 73442 ▁instagram 73440 ▁függ 73438 nevez 73435 ▁museum 73435 ▁verschidde 73435 ▁შეი 73435 chilari 73434 humana 73434 ▁gefa 73434 ▁llib 73434 ▁wyjątk 73434 niejszych 73431 ีด 73431 ▁filo 73431 ▁krvi 73430 við 73428 ▁Gaza 73428 ▁алдында 73427 ▁માણ 73427 ▁پایه 73426 ▁හඳු 73426 ▁Bald 73425 ▁ХХК 73425 ▁زندان 73424 ▁labiau 73420 บัญชี 73419 ▁běž 73419 ▁البلاد 73419 away 73418 чио 73418 ▁жовтня 73417 ▁загально 73417 зије 73415 פיה 73414 ▁ਨਵੀਂ 73413 することで 73413 ▁kriminal 73410 ケット 73410 ▁Kuma 73409 ▁људ 73408 ▁зго 73407 ▁свега 73407 ▁المؤس 73406 ▁მილი 73406 ▁cristi 73404 ▁fyz 73404 ▁допу 73404 ▁Import 73403 ▁පව 73403 petto 73402 ▁matr 73402 ແຫ່ງ 73401 ▁көндә 73401 ▁минимум 73399 ▁блоки 73397 後に 73397 íochtaí 73396 ▁asja 73396 ▁opti 73395 ▁Husk 73394 ▁ідэ 73394 ryd 73392 utie 73388 ਟਰੀ 73387 ▁možemo 73387 ▁olulis 73385 ▁bakar 73384 akukan 73383 ▁Gòn 73383 havn 73382 ക്കേ 73382 ▁کریم 73381 ▁ტყ 73381 elija 73380 ▁čim 73380 стран 73379 ▁Yahoo 73377 ▁ngaph 73377 ▁žiad 73377 ▁һуғышы 73376 ಕ್ತಿ 73375 ▁mazwi 73374 DV 73373 внова 73373 чното 73373 ходження 73371 atot 73370 ▁مخالفت 73370 роваться 73369 ólica 73368 ▁tantu 73368 šais 73367 ביבה 73364 ▁dvou 73363 ▁shekaru 73362 ▁татар 73361 ▁natierlech 73360 ▁oportunidades 73360 ▁ಮತ 73358 ▁Women 73357 ▁afor 73357 แปล 73352 ደረገው 73352 ▁TË 73352 asst 73351 ▁toisen 73351 holdt 73350 ▁անվտանգ 73350 elserne 73349 និយ 73349 μενα 73348 държа 73348 혼 73348 ▁Hee 73347 ▁በብ 73347 ળે 73346 宽 73345 estiu 73344 ตํา 73344 ▁taqdim 73344 ▁verschiedene 73344 ▁ماشومانو 73344 icima 73342 বর 73342 теді 73341 ▁दर्ता 73341 ▁vů 73339 onnal 73338 ▁બનાવ 73338 ▁دانست 73337 ynas 73336 ▁calidade 73336 ▁patvirt 73334 ▁Jezi 73330 ografic 73328 məti 73327 ▁поте 73327 ▁શબ્ 73325 க்கின்ற 73324 երմ 73322 ▁kados 73322 ▁janm 73321 sou 73319 ържа 73319 ▁ڏٺو 73319 домо 73318 ▁미래 73318 ▁صنایع 73314 ▁Akka 73313 ▁gràcies 73312 ▁notar 73312 ▁βιβ 73312 ▁grab 73310 ▁альбо 73310 ▁नहु 73310 ▁đo 73309 ▁бэлтг 73308 14] 73307 ेंड 73307 URS 73306 ब्र 73306 iaid 73304 たちは 73304 ▁úspě 73302 בלי 73301 ▁mhòr 73301 ▁යැයි 73301 ▁terren 73298 ▁zwou 73298 ▁سریع 73298 ▁περισσότερα 73297 ▁ଶ୍ରେ 73297 飾 73297 ີກ 73294 ពលរដ្ឋ 73293 itzer 73291 ▁samkvæmt 73291 ▁dobiti 73290 ▁relev 73289 ▁екра 73289 ▁Rīgā 73287 ▁nesse 73287 巡 73287 ▁räk 73286 лій 73285 社会主义 73285 isać 73284 ออกแบบ 73284 ▁લગ્ન 73284 AMBO 73283 ኙ 73283 ładu 73282 тырып 73281 τικο 73280 ▁6,5 73279 ▁lake 73279 স্ক 73276 ▁ಯುವ 73276 ▁pendapat 73274 ▁заврши 73271 -00 73270 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▁atyre 70641 ▁pensando 70641 udni 70640 ▁prečo 70640 ▁matt 70639 特定 70639 ▁demek 70637 ▁speed 70637 ▁mannalon 70636 ▁נשים 70636 ▁존재 70634 ▁leedahay 70633 ▁чинов 70633 ▁Dateien 70631 engs 70630 iry 70630 ▁Dirección 70630 ▁rikt 70627 נטר 70626 ▁۱۶ 70626 ▁qabe 70625 夕 70623 ▁Vál 70622 crire 70621 ▁ΤΟΥ 70618 meng 70617 ▁કપ 70617 راہ 70616 ▁Déc 70613 ▁hanyar 70612 ▁galer 70611 ▁አቅ 70610 ▁mân 70609 kowa 70605 ▁표현 70605 ternet 70604 ▁Hệ 70602 ▁termina 70602 ingozi 70600 irə 70600 minin 70599 itne 70598 ▁Bör 70597 ▁верну 70597 ▁vermek 70596 ▁csomag 70594 ▁sông 70594 ▁अल 70594 ▁istiqamət 70593 אַץ 70592 लाइन 70590 ምንት 70590 ▁síðustu 70589 ▁ಮಗ 70589 zoek 70588 മല്ല 70588 URL 70587 мка 70587 ▁mụn 70587 ▁etadi 70586 alisering 70585 ▁הבל 70585 цент 70584 allik 70582 ▁familha 70581 chang 70580 ▁simt 70580 ▁Saul 70578 ▁Daawo 70575 ▁ਕੱ 70575 음을 70573 ▁причин 70572 ▁ўста 70572 чности 70569 ▁prøver 70567 ▁Kyr 70566 ▁führen 70565 ▁keny 70564 ▁lisusu 70562 arci 70559 ▁нөхцөл 70559 ▁інтере 70558 ▁hyöd 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70407 ▁obitelji 70407 ▁समाधान 70407 ▁fisik 70405 ▁мл 70403 ▁स्तर 70403 ▁escuela 70402 ▁кратко 70402 ▁descoper 70398 ▁ezip 70396 ▁бірге 70394 ▁Solar 70393 ▁úts 70392 ▁DOS 70391 ▁جائز 70391 ▁κυκλο 70390 ▁IQ 70389 ▁Büyük 70388 чыцца 70385 ▁segle 70385 ▁těž 70385 yssä 70384 ▁Charlie 70383 ▁Kelantan 70383 ▁Senin 70383 ▁жить 70383 ▁মুক্তি 70383 тиот 70381 ▁Hvala 70381 ▁kett 70381 ▁બાબ 70381 artzeko 70379 ▁rozší 70379 ▁دیکھا 70378 ▁päivää 70375 ▁woorde 70375 ▁września 70375 ▁skaity 70374 моў 70373 ▁fungerar 70373 ▁밀 70373 ▁kunjalo 70372 ▁Alp 70371 ▁Need 70371 ▁ընկ 70371 ▁mėg 70370 ▁sinun 70370 ▁δραστη 70370 ▁структур 70369 ▁दूस 70369 ▁कार्ड 70366 ▁Qeerroo 70365 ▁ældre 70365 beren 70364 wezi 70364 ▁nafar 70364 ▁Izv 70363 ▁असतो 70361 พวกเขา 70360 ▁övers 70360 ▁економи 70360 ▁користува 70360 ▁dach 70359 ២០១ 70357 ▁Palace 70357 ▁regj 70357 ▁أهل 70356 ▁campanha 70355 ▁перад 70355 ukup 70354 ▁从 70354 بين 70352 हां 70352 ▁aming 70352 ▁чай 70352 ▁موتور 70351 ಾಲಿ 70349 ▁۲۰۰ 70348 ▁label 70346 jna 70345 ▁antiga 70345 ಷೇ 70343 ▁Cerita 70343 ▁aper 70343 ຮ່ວມມື 70342 לול 70341 ψης 70338 ▁acte 70338 ▁वाल 70336 ▁Phar 70335 ▁izmant 70335 ▁tumb 70335 ▁wymaga 70333 ▁አቀፍ 70333 楽しみ 70333 ▁unap 70330 ▁1932 70328 ▁pesquisa 70328 ―― 70327 ▁gason 70327 ▁MH 70326 ▁Бұ 70326 ▁დეკ 70326 扣 70326 isil 70325 여행 70325 ▁помочь 70322 ▁솔 70321 가능 70321 nę 70320 럭 70320 sın 70317 áron 70317 习惯 70316 ▁čini 70314 ▁Schule 70313 ān 70309 ▁mucha 70309 ▁Жур 70308 ▁Мари 70306 ▁जमा 70306 ▁რაი 70304 ליות 70303 ▁велика 70303 ▁מיטוואך 70303 ▁tē 70302 ပြု 70301 ▁priestor 70301 ▁inkl 70300 ▁volle 70300 ▁तुम्ह 70299 drar 70298 គ្រឿង 70296 ▁dict 70295 ▁düzenlenen 70295 जल 70294 ▁týždeň 70294 ためには 70293 老人 70293 者は 70293 сыг 70292 ▁spielen 70292 ▁Toe 70291 ▁επανα 70289 ▁فعل 70289 ▁zpráv 70287 ▁egyetlen 70286 asing 70284 ▁επειδή 70284 ▁bliz 70283 ▁учреждение 70283 ▁پرست 70281 ▁सहज 70281 ▁Sumber 70280 ▁dňa 70280 ▁אַן 70280 ▁përball 70279 봤 70279 ರಳ 70278 žić 70277 ▁babai 70276 ▁Diana 70275 ▁pinaka 70275 ▁Петер 70275 )[ 70274 رمز 70272 וחה 70271 ▁Министерството 70270 ▁изд 70270 baren 70269 ighinn 70269 фото 70269 ▁gennaio 70269 โปรแกรม 70268 دث 70265 ▁1/4 70264 uleko 70263 ▁Orta 70263 ▁vältt 70262 ▁yikuphi 70262 waarden 70261 ▁adieraz 70260 ▁આજ 70260 jiwa 70257 ▁turan 70257 กลง 70256 ▁light 70256 ▁किती 70256 ▁rychle 70255 ▁өзге 70253 ▁വിജയ 70251 ניסה 70250 wró 70247 কাল 70246 ▁Force 70245 ▁résultats 70245 ▁köszönh 70244 ▁խնդ 70244 ्दै 70243 ▁ዝግ 70243 باز 70242 डियो 70242 ▁Hrv 70241 uther 70239 ▁आवश्यकता 70239 專業 70239 koma 70238 ▁Berkualitas 70238 ▁tôn 70238 ▁Javier 70237 əncə 70234 ازي 70234 ▁aine 70234 ▁PART 70233 ▁pamp 70233 ▁ହି 70233 ាល័យ 70232 ▁நாடு 70231 ▁Kedah 70229 ▁បង្ហាញ 70229 ロード 70229 竞 70229 ənn 70228 ர்ந்து 70228 กด 70226 ▁ይመስ 70226 ydä 70222 álních 70220 ▁NEW 70220 ▁विद्या 70219 yddiaeth 70218 OLE 70216 ▁Zapraszamy 70216 ▁மாத 70216 保存 70216 ▁січня 70215 ▁танилцу 70215 Ż 70214 udla 70212 ▁kelgan 70212 ▁Sempre 70211 ▁קומ 70211 отно 70209 ▁Ден 70209 iaidh 70208 ▁APA 70206 ▁движение 70206 ▁فول 70203 ▁مدرس 70203 ▁bongo 70202 ▁článek 70202 ▁संस 70200 代理 70200 ▁юлы 70199 ▁שעה 70199 ▁ବୈ 70199 ▁ayant 70197 ▁Δείτε 70195 ▁وض 70195 javi 70194 ụlọ 70187 ▁astăzi 70187 ekon 70186 ▁സന്തോഷ 70186 qhub 70185 ▁sığ 70184 召开 70182 ▁למח 70180 লাই 70179 ▁gidan 70179 ▁изклю 70178 ▁полага 70178 gled 70176 ▁filmes 70176 ▁письмен 70176 ▁өҫтә 70176 ▁않았 70176 早く 70176 特朗普 70174 最好的 70173 ▁izr 70172 കൂ 70171 adak 70168 ▁पदा 70167 vislosti 70166 ▁შინა 70165 ▁hossz 70164 ▁låg 70164 ▁xấu 70164 ▁condições 70163 ▁terlibat 70163 ▁tòa 70163 ▁것도 70161 სენ 70160 ▁Hoje 70160 ▁MAC 70159 ▁Kär 70158 ▁berusia 70158 ர்த்து 70157 ouz 70155 ulette 70153 ▁Haziran 70152 ▁apoyo 70151 20) 70150 ຕົກ 70150 ▁ລາ 70148 ▁Sevilla 70147 ▁Hie 70146 мень 70144 ▁göstərir 70144 Pet 70143 ▁SEB 70143 ▁bangun 70142 ▁ರೋ 70142 ▁katerem 70140 ▁Йыл 70139 ಗೊಳ್ಳ 70138 ▁nesre 70138 ▁общества 70138 ▁інте 70138 lol 70137 arag 70135 ▁Coca 70135 ▁KATIKA 70135 ▁läks 70135 штары 70132 क्षित 70131 ▁mãe 70131 idai 70127 ▁djelat 70127 ▁sağlay 70127 ▁રાજ્ય 70127 ស្ត 70126 buu 70125 ▁sniedz 70124 ▁പുസ്ത 70124 お店 70124 ▁heli 70123 От 70121 ங்கா 70121 ▁olunması 70121 ungkap 70118 ▁præs 70118 eceu 70117 *) 70116 ▁σελίδα 70115 行われ 70114 שרת 70113 ▁Onko 70113 ▁především 70113 ▁යුතුය 70113 ▁המא 70112 orca 70111 tran 70111 କ୍ରମ 70111 ▁iepaz 70111 тена 70110 ▁हामीले 70109 ▁കൂട 70108 kär 70105 ▁عمار 70105 žiu 70104 ▁darbības 70104 Жа 70102 otājiem 70101 ▁2-0 70101 ▁KUL 70101 ▁lít 70101 ablaak 70099 ▁Jl 70099 ▁реформа 70099 कोट 70097 ▁pelayanan 70096 ▁Гро 70096 ▁дати 70093 ▁በፍ 70092 ▁תחת 70091 ▁нати 70090 magan 70089 eix 70087 ▁οποίες 70087 かも 70087 දින 70086 ▁напълно 70086 ▁Konz 70085 ▁કર્યા 70085 ahet 70084 ո՞ 70084 ▁dorë 70083 ▁запад 70082 ▁campos 70081 ▁nainen 70081 በሩት 70080 ুজ 70078 čným 70076 ▁artikkel 70076 ▁duid 70076 जूद 70074 ▁mógł 70074 ▁sinulle 70074 ▁პროფ 70074 ▁Heim 70073 ▁ಮಾಡಲು 70073 ▁Aonta 70072 ▁Wort 70072 ▁født 70071 นวย 70070 ▁perdu 70069 ▁мақсаты 70068 ▁olduk 70067 ▁இருக்கிறது 70065 жам 70064 وید 70064 ▁geschikt 70064 ▁miljoner 70064 ▁ચૂંટણી 70063 ▁TIN 70060 ▁valenci 70059 ▁huyết 70057 aldade 70056 bildung 70056 ດັ່ງກ່າວ 70055 ▁organisatie 70055 ▁આમ 70055 ▁Tach 70054 aynta 70053 ▁வெற்றி 70053 ooɓe 70052 ▁informação 70052 ችው 70050 ▁UNA 70049 ▁segít 70049 த்தம் 70048 摸 70048 ▁depoz 70045 ▁نیروی 70045 ▁ফোন 70045 ▁142 70044 ▁tijela 70044 ▁Voit 70043 ▁Earth 70041 ▁merak 70040 ehér 70039 essel 70039 ▁Qay 70039 ▁negali 70039 大利 70039 ▁oi 70038 ▁נמ 70038 кула 70037 ▁រាជធានី 70037 ▁аудио 70036 جاه 70034 ಚಿ 70034 ▁később 70034 ਸਮ 70033 ▁Nod 70032 ▁රනිල් 70030 τήρι 70029 olgál 70027 ▁científica 70026 ▁хро 70026 ไปด้วย 70025 новите 70024 ▁histoire 70024 ▁mv 70024 ▁hest 70021 ▁Vinh 70020 ▁afar 70020 ▁capire 70020 ▁просты 70020 ▁رائے 70020 ▁ገንዘብ 70020 ▁לענ 70019 ▁בעיקר 70018 ▁süd 70016 ▁territo 70016 ▁ефект 70016 ▁Ier 70014 ▁мүше 70014 ▁أت 70014 десет 70013 ▁Benz 70012 ▁משח 70012 raichean 70011 ▁Rig 70011 ▁어디 70011 ▁inneb 70009 ▁ամս 70009 ▁menej 70006 ▁pasos 70006 idler 70005 venie 70005 ▁facult 70005 ▁rival 70005 ▁propu 70004 致します 70004 لیت 70003 ▁skrá 70002 ▁çöz 70002 ▁skle 70001 ▁хийг 69999 บริเวณ 69998 ▁ქართვე 69995 ▁насла 69993 ▁כדאי 69992 ▁руку 69991 서울 69988 ▁académ 69986 ▁darbe 69984 ▁valand 69984 urot 69983 ▁alalan 69983 ▁davom 69983 ▁depos 69983 ▁ಅಂದ 69983 ▁അമേരിക്ക 69983 ▁petites 69981 лод 69980 ▁Hvil 69979 ▁sàn 69979 ටුව 69978 ▁jula 69976 ovými 69974 ▁Abbas 69974 ▁veli 69973 beth 69972 ▁50.000 69972 ▁notte 69971 UDI 69969 ▁Kepada 69969 ▁Peru 69969 ▁ويم 69968 ▁maggi 69967 ▁ungg 69964 ▁diagn 69963 дине 69961 ▁seres 69961 ▁değiştir 69960 ▁vorg 69960 ▁títulos 69959 最好 69959 ▁ced 69958 ▁vitamina 69957 larimiz 69956 hing 69954 ▁हिन्द 69954 ▁байгуулах 69951 ▁ресторан 69951 ▁Gub 69950 ▁meilleur 69950 umani 69948 ▁שש 69948 annaa 69947 ▁angl 69947 ▁ዓይነት 69947 ▁കീ 69945 ▁엔 69945 ЦЕ 69943 зирован 69943 ▁ярты 69943 ታደ 69938 ▁kompens 69938 ▁sustav 69936 zeka 69934 ▁бодлого 69933 ▁ender 69931 ▁haten 69931 ▁ആള 69930 манитар 69929 نوات 69929 ▁Bren 69929 ▁Турция 69929 kede 69928 ▁تعلي 69927 unyaa 69925 ▁kwenu 69925 TK 69924 kring 69924 ▁157 69924 ▁інстру 69921 ▁tayyor 69919 ▁maip 69918 ▁подели 69918 یش 69917 ▁Jetzt 69917 ▁kasano 69916 ▁выполня 69916 ▁כזה 69916 ਜਨ 69915 lå 69914 ▁senge 69914 ▁stè 69914 ▁Help 69912 ▁rozwoju 69912 ▁trafic 69911 ▁standar 69910 ▁dersom 69909 ▁Унда 69909 צוב 69908 ▁выи 69907 ▁결제 69907 லோ 69904 ▁położ 69904 elijks 69902 ▁Публи 69902 tersom 69901 ▁spomin 69901 ▁matsayin 69899 ▁Universidad 69898 ▁kahd 69896 ▁кешен 69896 ▁organizacije 69894 nk 69892 לעכע 69892 ▁saska 69892 udian 69891 ▁osam 69891 ▁portar 69891 ýn 69887 ▁kedai 69887 ▁inspiration 69886 alım 69885 mess 69885 ▁անել 69885 개의 69884 vás 69883 ▁bros 69883 ▁mutlu 69882 ▁sortida 69882 ▁والث 69882 ▁කේ 69882 ਿਸ਼ 69880 ▁alternatif 69880 ▁yarım 69880 ▁פֿר 69880 ▁beş 69879 ▁גיל 69876 ▁อ่าน 69876 ▁kuitenkaan 69875 allista 69874 ▁Kristian 69874 ▁llingües 69874 κος 69873 ▁lái 69873 öä 69871 giad 69870 ▁адзна 69869 ▁масо 69869 ▁ଗଣ 69868 ▁Liefer 69867 ▁viskas 69867 ▁чыта 69867 ▁definir 69864 終了 69862 ▁پرداز 69860 színű 69859 թաց 69859 ▁avalia 69858 ҙәренең 69857 ឯក 69857 istai 69856 ੁੱਟ 69856 utna 69855 ▁amos 69855 ▁Ур 69853 सरी 69852 ▁ਉਪ 69852 ▁ញ 69852 ▁וג 69851 ießen 69850 ▁سج 69847 တပ္ 69845 bland 69844 ▁هوندا 69843 úe 69842 -300 69841 ամաս 69840 ლიან 69840 ▁kadr 69839 アー 69839 ▁چال 69838 ÍA 69837 ▁Olet 69837 ▁energii 69837 關係 69836 ▁биће 69835 ▁જણાવ્યું 69835 vára 69834 τουν 69833 ▁aktivitas 69832 ▁පුරා 69831 äf 69829 ▁մյուս 69829 ▁എല്ലാം 69829 ▁Yaş 69828 ▁lelo 69824 lima 69823 ▁which 69823 ▁professionals 69821 ▁رهبری 69821 сници 69820 یان 69820 ▁Segunda 69820 ▁labari 69820 ▁Status 69819 ▁немец 69819 ונדער 69818 ▁zobra 69817 ▁штам 69817 chodzą 69816 ▁amaç 69816 ▁mangg 69816 ▁декаб 69816 ▁ਯੂ 69815 ërit 69809 ▁Həsən 69809 ▁empezó 69808 بران 69805 ▁руски 69805 ▁انهي 69805 ▁高 69805 ▁piso 69803 ଙ୍ଗା 69802 ▁fui 69802 essor 69801 ▁Qever 69801 ▁pender 69800 гдах 69799 ▁HC 69798 ecuti 69797 ▁Хоро 69796 ▁kalit 69795 ▁poka 69795 ▁ଓଡ଼ିଶ 69795 ngay 69794 ▁zituzten 69794 खंड 69793 ▁exces 69792 ▁mogućnosti 69792 ▁മുഴുവ 69792 씩 69792 ▁św 69791 采用 69791 ▁Đầu 69790 ▁1897 69789 ▁направите 69789 ▁केसी 69789 ተና 69788 ▁Tato 69787 ▁asociación 69787 ▁pohon 69786 ▁proj 69785 는다 69785 ▁jist 69784 ូតូ 69783 ▁podstawie 69783 ▁געווי 69783 YL 69782 ▁chol 69781 ▁95% 69780 ▁ấm 69780 ▁Depuis 69778 ▁γον 69778 ▁सत्ता 69778 ▁naturales 69777 omie 69775 ▁iako 69772 ▁urusan 69771 ▁낮 69769 ▁warra 69768 ▁Moun 69767 ▁gelo 69767 ▁अन्तर्राष्ट्रिय 69766 ▁धु 69765 ▁نباید 69764 してくれる 69763 ▁კონს 69762 ulula 69759 ඵ 69759 ▁hetkellä 69758 ▁tilk 69758 urum 69757 ▁sòn 69756 ▁cieľ 69755 ▁Canal 69754 ▁trailer 69754 今回は 69754 ▁ilan 69753 ▁كند 69753 ▁મહિ 69753 /24 69750 imbag 69750 ▁ୱ 69750 rein 69748 ▁dhuga 69748 汽 69748 ▁Rydym 69747 ▁velger 69747 ρές 69745 ▁लगायत 69745 ▁بگذار 69744 ▁Teilnehmer 69742 ▁задол 69742 ▁Raim 69740 ▁kimia 69740 ▁reći 69740 ▁সম্পর্কে 69739 pier 69738 ау 69738 ▁Econ 69738 ீர்கள் 69737 ▁envolv 69737 ▁uYehova 69737 やはり 69735 likheid 69734 ٨ 69734 दाता 69734 เจ็บ 69734 ▁namanya 69734 ▁track 69734 ▁mừng 69733 ▁օգտ 69733 ዐ 69732 autom 69731 ဆိုတာ 69730 ▁wert 69730 価値 69730 ▁запазени 69729 ▁دانشجویان 69728 êg 69727 એમ 69727 ▁допуска 69726 эгт 69725 ▁hours 69724 ▁хойш 69724 čnú 69722 əlli 69721 ▁marche 69721 rass 69720 ▁Prvi 69718 ▁teik 69717 ▁брой 69714 ▁مرور 69711 ▁János 69710 ▁parfum 69710 대학 69710 ējais 69708 jeun 69707 ätten 69707 ได้ที่ 69706 ▁ځواک 69706 ▁hank 69705 ▁метро 69704 вня 69702 ▁megt 69702 医生 69702 ▁Toki 69701 ▁آدرس 69701 ▁ਹਾ 69701 ▁ofri 69699 ▁სამართ 69698 ujuk 69697 ຄິດ 69697 สไตล์ 69696 ▁යනු 69696 здар 69694 ▁putra 69693 ▁użyt 69692 ▁ласка 69692 ▁Պետ 69692 راس 69690 ▁התמ 69690 ნელი 69689 ▁spillere 69689 ården 69688 ویش 69687 ▁Lịch 69685 ▁Rabu 69684 ▁Uyg 69684 ອັນ 69683 ▁evro 69681 ▁verge 69680 ánky 69679 elola 69678 ▁ຕຸ 69677 ▁οτι 69676 ▁estiver 69672 ▁փոքր 69670 ▁berhenti 69669 ▁ragazzi 69669 ▁ឆ្នាំ២០១៨ 69667 ▁174 69665 ▁линия 69665 ▁tider 69664 arbejd 69663 ▁پورته 69663 štvu 69662 ▁Jard 69662 ▁materiais 69661 YI 69659 govor 69658 kval 69658 กัด 69658 ▁afara 69658 ▁onver 69658 ▁חברות 69658 ▁සිටියේ 69657 ▁Ọló 69657 盡 69657 ▁aflat 69655 ▁ഹാ 69654 ▁영상 69654 ▁nevid 69653 ▁memahami 69652 ▁piace 69651 ▁subiect 69651 ▁ეკონომიკ 69651 נויות 69650 appy 69648 ljanju 69647 diugh 69646 ที่เรา 69645 ▁Κό 69644 wysig 69643 ▁atr 69640 ▁kk 69640 ▁күсе 69640 ▁Austri 69635 ▁València 69635 ▁Ерөнхийлөгч 69635 ▁вещи 69633 ▁එකතු 69633 եթ 69630 ให้บริการ 69629 ▁ortam 69629 ▁rapide 69628 ▁Luxemb 69626 ▁формы 69626 ildar 69625 मल 69625 Мы 69623 ▁kalayan 69623 σον 69621 ▁Kent 69619 เด่น 69618 ilihan 69617 itė 69616 ▁דארט 69616 ▁თბილის 69615 ▁përk 69614 ▁допомоги 69614 ▁ہوگی 69614 dienu 69613 ரீ 69613 warga 69612 ▁کرک 69612 ▁јасно 69610 ▁બીજા 69610 azak 69608 ຫນີ 69608 ▁ratka 69608 ▁Gond 69606 ▁exer 69605 ▁Aš 69604 ▁Lif 69604 Fa 69603 ▁Omega 69603 ▁sezn 69603 ▁ներդր 69603 ▁scéal 69602 どの 69602 ▁NK 69601 ▁ፍርድ 69601 ▁ຈໍາ 69597 宾 69594 mán 69593 ▁ಶ್ರ 69592 ungit 69591 ෝද 69587 ບາຍ 69587 ▁Haha 69587 ▁рә 69587 ▁principali 69586 ▁غو 69586 ▁мөл 69585 ethemba 69584 每次 69583 ▁dolu 69582 บูรณ์ 69579 ▁bryd 69579 berte 69578 ới 69578 ദന 69577 ທິດ 69577 ▁contun 69575 ▁protok 69575 っていました 69575 seh 69574 ▁comuni 69574 ▁צווישן 69573 以为 69571 unie 69567 ▁participants 69566 ▁↳ 69566 ▁Основ 69565 ▁ਬੈ 69565 føres 69563 ▁Alkohol 69563 ▁Ըն 69562 ରାଜ 69561 ▁insanın 69561 ▁lū 69561 ▁rind 69560 ▁sice 69560 த்தது 69559 ▁žmogus 69559 ména 69558 urlar 69558 ላፊ 69558 ▁팔 69556 ▁Seminar 69555 әләре 69553 ▁segi 69553 ▁Logg 69551 atska 69550 najm 69549 ▁dicas 69548 pann 69547 रेल 69547 ▁Kedua 69547 ▁घाइते 69546 න්දර 69545 दछ 69541 ▁सबैभन्दा 69540 czny 69539 escri 69537 ▁Yes 69535 ▁trimestre 69534 ▁пис 69534 ▁Dipl 69533 ▁kaufen 69533 ▁measc 69532 днай 69530 ▁Erg 69530 ▁během 69530 ▁ngani 69530 ▁nida 69529 ▁verilib 69529 ▁चुन 69529 ंह 69528 ▁ICT 69528 ▁(2010 69526 ▁gegeben 69525 ▁teist 69525 ▁мрежи 69525 ikiem 69522 ▁Dị 69522 ▁हटा 69519 ▁bolingo 69517 ▁joue 69517 leleni 69516 ▁gwybodaeth 69516 ▁History 69514 ▁للع 69514 वरून 69512 ▁заў 69512 ▁Башлығы 69511 ▁अवस्थामा 69511 skriv 69510 integr 69509 ▁śmier 69509 ▁credit 69508 ▁критери 69507 ▁розных 69507 स्प 69505 вілі 69503 誕 69503 ▁piv 69502 ▁fhaighinn 69500 ▁mühüm 69499 ▁دچار 69499 ṣé 69498 agad 69496 ▁facă 69496 ünst 69495 ▁154 69494 ▁Julai 69493 ctu 69490 ▁canc 69490 ▁পারেন 69489 ISE 69488 ▁bart 69488 ▁ឆ្ល 69488 ▁wech 69487 ▁характеристики 69487 skú 69486 ▁Rotterdam 69486 किंग 69484 ዳር 69484 առում 69483 ▁spetta 69483 anggo 69481 ▁waarvan 69481 ▁İşte 69481 递 69481 nant 69480 ರಾದ 69480 ▁Rolle 69478 льнікаў 69477 ▁coach 69477 ▁thoughts 69477 कृत 69474 ▁konkre 69474 ▁રૂપિયા 69472 東西 69472 ▁njemu 69470 ▁શેર 69470 viser 69469 üng 69467 ▁alternative 69466 ▁tunggu 69465 ▁حملے 69465 ▁warum 69464 ければ 69464 ριος 69463 ▁kanneen 69463 ▁unb 69462 ▁יאָר 69462 ▁أيام 69462 ▁asiakas 69461 ▁onkin 69461 ▁slē 69461 ▁βρα 69461 ▁PlayStation 69459 ▁بھائی 69459 ▁ingenting 69455 ▁засобів 69454 ▁хужалыҡ 69454 ยน 69453 ▁охорони 69453 插 69453 ▁различные 69452 ▁bones 69449 ▁yata 69449 ▁bedst 69448 ▁uti 69448 ▁vijf 69448 的不 69448 ▁nash 69447 ▁erforder 69446 ▁lazima 69446 БЕ 69445 ▁системе 69443 ▁ಟೆ 69441 ▁සදහා 69441 џе 69440 ▁പ്രാ 69440 yung 69438 ▁narš 69438 teh 69436 слаб 69436 ▁ունենալ 69436 ▁શહે 69436 ège 69435 ▁talle 69435 famil 69433 ovića 69433 ▁хол 69433 ▁दिनों 69433 adow 69432 alatan 69430 ▁ciuda 69430 ennek 69428 ▁fact 69427 ▁બાળકો 69427 ▁chiad 69425 ▁solen 69424 ybes 69418 ਿਲਮ 69418 ▁dvoch 69418 ▁κρί 69417 χθεί 69416 重庆 69416 ▁خرچ 69415 ▁نحن 69415 ▁uzoba 69413 伙伴 69413 ▁mold 69412 ביעה 69411 รูปแบบ 69411 過程 69411 entă 69410 ▁чиг 69410 ▁កើត 69410 ▁tilby 69408 ▁ñalnde 69408 τίες 69406 ▁оң 69406 ▁facem 69404 ordre 69403 തിന 69403 ▁साथै 69402 5,000 69400 ▁kaupungin 69399 ▁Ären 69399 baba 69398 ଚ୍ଛ 69397 álneho 69396 ▁ganar 69396 ▁йәй 69396 भात 69395 ですので 69394 원은 69394 які 69393 អ្វី 69393 France 69392 валіся 69392 ▁السياسي 69392 zona 69390 ΠΟ 69390 ▁선정 69390 zetten 69389 ▁guruh 69389 ਭਾ 69388 ▁pavyzd 69387 ▁خواهید 69387 atuu 69385 èria 69385 ▁Ener 69384 ▁kösz 69384 ▁stag 69384 6). 69383 ▁Croatia 69382 ▁colore 69382 Jaa 69381 ▁наші 69381 Bel 69380 ulod 69378 veel 69378 ▁QR 69377 ▁entorno 69375 ▁SUV 69374 ▁πρόσω 69373 ੇਰ 69371 éket 69370 edige 69368 راءة 69367 ▁nyumba 69367 ▁Professor 69366 ▁amad 69366 лерге 69365 记录 69365 рти 69364 mutat 69362 zm 69362 /? 69361 ალა 69361 ▁vermiş 69361 ▁குறிப்பி 69361 ▁Adrian 69360 ▁sesta 69360 撃 69359 pena 69357 ▁entste 69356 старын 69355 ▁pastor 69355 ▁շարք 69353 지가 69353 urde 69352 ▁február 69352 ▁σημε 69352 gali 69351 صالات 69351 ▁Daarnaast 69350 ▁መድረ 69348 ▁२२ 69347 ιω 69346 ▁ځواب 69346 ▁Wam 69345 ▁मिळा 69345 ndes 69344 ▁bulu 69343 训练 69343 ▁Sofia 69342 ▁നിന്നു 69342 ▁ഘ 69341 ŋa 69339 बू 69339 elės 69338 14, 69336 uos 69336 ี่ยน 69336 ▁1997. 69336 ីរ 69335 ▁ölkənin 69335 عکس 69334 ▁எதிர்ப 69334 ▁প্রশ্ন 69333 ▁müdafiə 69331 ▁аргу 69331 ▁qofa 69330 ▁Rapport 69329 ▁pieno 69329 遍 69329 ▁procha 69327 音楽 69325 យៈពេល 69324 ▁liburu 69324 アン 69324 ▁внесен 69321 ▁Podob 69320 ▁লাগ 69320 سره 69319 aniel 69318 ֲ 69318 ▁dõi 69318 ▁победа 69318 ▁سعی 69318 ▁Gruppen 69316 ▁ਭੇ 69316 cius 69315 ▁ajutorul 69315 живать 69310 ▁അഭിന 69310 ▁члена 69308 ▁veebile 69305 ▁אוכל 69305 utzer 69304 ▁skai 69303 livosť 69302 στάσεις 69302 ▁czasem 69302 ▁offerte 69302 ドラ 69302 aree 69301 ▁butuh 69301 ▁grop 69301 ▁zgodnie 69300 鏡 69300 ड्या 69299 ▁sven 69297 ▁Haa 69296 ▁peal 69296 ▁saks 69296 تری 69295 ▁jakim 69295 ▁çağır 69295 ▁सत्य 69295 ▁października 69294 fora 69293 ▁претен 69293 farë 69292 ാനി 69290 ▁mesures 69289 ्रै 69288 ▁бөт 69288 လင္း 69287 ▁implant 69287 ▁saur 69287 ▁Έτσι 69285 ▁takmer 69283 ▁दूसरे 69281 ▁lugha 69280 ▁істори 69279 ▁dokumenti 69277 ▁nepri 69277 ▁გულ 69276 ▁2017: 69274 ▁krém 69272 ▁관광 69271 йшов 69270 ▁sani 69269 ▁հենց 69269 ല്ലാതെ 69268 ଂହ 69267 ▁Toim 69266 ▁знает 69266 izate 69265 ▁хэс 69265 ▁cord 69264 чине 69263 ▁ادبي 69263 ▁konsek 69262 ▁joške 69261 tsp 69259 зив 69259 ▁прими 69259 ▁Paras 69256 क्शन 69255 urga 69253 ▁całkow 69253 ോക 69251 èv 69250 õttu 69250 ويات 69250 โจ 69249 ▁zodpoved 69249 ▁Ќ 69249 ທະຍ 69248 ▁ansin 69248 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68345 ▁redi 68345 ینت 68344 ▁mưa 68343 ímp 68342 ▁naannoo 68342 ▁Presidenti 68341 ▁Akin 68339 ▁rendelet 68339 hör 68338 ▁fyrr 68338 ▁kommit 68338 ▁падзе 68338 ▁især 68336 ▁Fransa 68333 aika 68332 ▁Universal 68332 ▁CZ 68329 ▁xir 68329 ▁súly 68327 ?- 68326 ▁jeans 68326 ▁Punj 68325 ▁ආර 68324 θημα 68322 បន្ត 68321 ▁erum 68318 ▁rapidement 68317 ▁انساني 68317 atës 68315 yte 68314 ▁Тан 68314 ▁आकर्ष 68314 ▁байгууллагын 68313 roveň 68312 ګي 68312 ▁35, 68312 ▁Pyr 68312 ▁mutum 68312 ▁Hvem 68310 ▁shpejt 68310 ▁басшы 68310 101 68309 සී 68309 ▁Padahal 68309 ▁புது 68309 ▁وغيره 68308 íche 68307 களின் 68305 ▁geniet 68305 pote 68304 ▁Europej 68304 로서 68304 odami 68303 quetas 68303 ▁ماحول 68303 ▁encuent 68302 ▁žino 68302 ▁দিতে 68302 ئيس 68301 ▁najviše 68299 ayso 68298 ිත් 68298 ▁በርካታ 68298 onsin 68297 ▁سرچ 68297 ಂತ್ 68296 דול 68294 ▁पुढे 68294 ▁ພາຍ 68293 ▁ხშირად 68293 czka 68292 wald 68292 ìomha 68292 -90 68291 ກອງ 68291 ▁upe 68291 ▁հերթ 68291 explic 68290 leda 68289 oyada 68289 ▁pytanie 68288 ▁پوښت 68288 ოგი 68286 otovo 68284 ▁omsorg 68283 fiq 68282 ▁իրեն 68282 stück 68281 ण् 68281 ällning 68280 ▁sistemul 68280 125 68278 energia 68277 ▁mobi 68276 റായി 68275 ukogu 68273 ▁mhór 68273 ▁Ethi 68272 ▁MEL 68272 ▁séjour 68272 ▁বিজ 68272 ▁zvol 68270 juar 68266 ▁pocos 68266 ▁खूब 68265 ▁неделю 68264 ແຜນ 68263 ▁хутка 68263 렴 68262 Ша 68261 әрә 68261 asaran 68260 ▁Зако 68258 ▁Günü 68256 ▁Halloween 68256 ▁म्या 68255 ▁বেশি 68255 ремо 68254 นาง 68253 ▁nano 68249 ▁ඇතුළු 68249 ▁колку 68248 ケア 68247 追加 68247 irr 68246 ▁Velik 68245 під 68244 ▁мекеме 68244 ▁ਆਮ 68244 ▁різ 68242 ▁өзгері 68240 cles 68239 ίκες 68232 ▁postęp 68232 ▁tegel 68232 ік 68231 ▁leben 68230 ▁ਸੇਵਾ 68227 ▁කාලෙ 68227 ugma 68225 ▁امنیتی 68225 의를 68225 ▁túi 68224 ▁Έλληνες 68223 ▁Honor 68221 ▁core 68221 ▁zanter 68221 ▁Любо 68220 finans 68217 ▁Благодаря 68217 ▁tlak 68215 ▁cœur 68214 ▁ocasi 68212 ▁ceb 68211 ницы 68210 ▁Түр 68209 fjord 68208 जार 68208 ▁imprescind 68207 ▁записа 68207 сход 68206 asyonu 68204 veckl 68201 ስር 68201 ▁erakunde 68201 ▁Dot 68200 ▁colori 68200 ▁fris 68200 ေတြက 68198 ▁උස 68197 ▁Bayram 68196 碰 68196 ▁யார் 68194 ▁നടത്തി 68194 ▁fejl 68193 大型 68193 ▁tuan 68192 ▁ash 68191 ▁actit 68188 ▁вест 68188 ອື່ນ 68186 になると 68186 ▁reyn 68184 ▁Swydd 68183 ▁қаты 68183 ▁საკუთ 68181 Ir 68180 ▁ritu 68180 ▁ryk 68179 ▁vehivavy 68177 ▁пассажи 68172 ▁өзінің 68172 ▁Family 68170 ACI 68169 ▁πόλη 68166 ▁зокрема 68166 ▁паль 68165 ▁беспо 68164 ▁tagit 68162 ტოს 68161 ▁konuştu 68161 hloko 68158 ▁Kadagiti 68157 ▁energía 68157 ിക്കാൻ 68156 ▁απαν 68156 ▁эсвэл 68156 ▁വ്യാ 68156 ”! 68154 ▁Dagens 68154 ▁руково 68154 ▁stumm 68153 othe 68152 ▁හමු 68150 ▁ראשי 68149 ▁역할 68149 ▁නොහැකි 68147 entrée 68145 ▁giàu 68145 ▁manch 68145 кары 68144 ▁орталығы 68144 ▁گوش 68143 გზავ 68142 ▁zám 68142 ezwe 68141 муть 68140 ▁මීට 68140 ңа 68138 ▁dauden 68138 ▁komplek 68137 ▁нашай 68136 ▁shuningdek 68135 ▁ਅਜਿ 68134 ▁faste 68132 ▁தன 68131 适合 68129 լին 68128 ▁ለማስ 68126 ▁Lain 68125 ▁இருக்கு 68125 เน็ต 68124 ▁ಪ್ 68124 ▁სამი 68124 ▁liksom 68123 ▁सङ् 68122 ▁évben 68120 ▁Ferenc 68118 ▁қызметкер 68118 ቻል 68117 ▁Køb 68117 ▁майбут 68116 ремен 68114 ▁balt 68113 ▁відсут 68112 ▁२०१८ 68112 ▁infr 68111 dagangan 68110 ▁İngiliz 68110 ▁гало 68110 vier 68109 спонден 68109 ▁abuz 68109 ▁შეგ 68109 ahaya 68108 ंडे 68108 ละคร 68107 clusive 68104 ଚିତ 68104 ▁thẻ 68104 ▁diskrimin 68097 เปอร์ 68096 ▁Gip 68095 ▁ਚੰਗ 68095 ิศ 68089 ▁თურ 68088 ▁Hefyd 68087 ▁કેવી 68087 ▁Aleman 68086 ▁Matem 68085 ▁pamilia 68085 鍵 68084 skum 68083 лене 68083 ▁организира 68083 праў 68082 ▁1.00 68082 ▁Jol 68081 ▁intro 68081 ▁ڀا 68081 ത്തിലും 68080 ▁المنتدى 68080 ▁Aza 68079 ربة 68078 लाइ 68078 ▁çıktı 68078 amua 68076 ▁izc 68076 ▁ستاسو 68075 ▁dispoñ 68073 زارة 68072 ▁prática 68072 ិការ 68071 ▁svojem 68071 ▁texte 68071 yminen 68069 ▁categor 68069 ▁ವ್ಯವಸ್ಥ 68069 ▁Lagi 68068 skrá 68066 ணு 68066 ▁atend 68066 ▁عین 68066 xtar 68065 ▁δώ 68064 ตําแหน่ง 68063 ▁ರಾಮ 68063 redu 68061 ▁elb 68061 ▁ଦି 68061 ▁iyada 68060 ▁मोठ 68060 ▁sihl 68059 Wat 68058 ▁places 68058 ▁dark 68057 ▁azalt 68056 ▁degmada 68056 ▁venter 68056 Press 68055 خان 68054 เบอร์ 68054 ▁ձեզ 68053 ▁하나님 68050 ▁располага 68049 лних 68048 परि 68048 ▁거의 68048 址 68047 週間 68047 özben 68046 ibang 68045 ículos 68044 ▁orixe 68044 ▁újabb 68044 gegevens 68043 idata 68043 软 68043 期的 68042 ▁prevede 68041 ▁한번 68041 ▁används 68040 ▁конститу 68040 ▁자동차 68040 resta 68039 ▁explo 68039 ▁uzvar 68039 ▁பார்த்து 68039 rès 68038 ▁asam 68038 linge 68037 ▁ស៊ 68037 ▁afgelopen 68036 ▁байра 68032 ▁ակտիվ 68032 ▁أعمال 68032 ▁Tolos 68031 ▁vogël 68031 ▁සැක 68029 ▁сторону 68028 cum 68027 ▁گاز 68027 ▁രാഷ്ട്രീയ 68027 orsi 68026 学会 68026 ▁tentar 68025 ▁אמערי 68024 ▁ಚುನಾವ 68024 ▁puţin 68021 ▁sond 68021 ▁נמצ 68020 ▁täit 68018 ▁jaką 68017 ▁ይላ 68016 ▁යහ 68014 ▁საპ 68013 ▁comunicazione 68011 ▁szemben 68008 ▁zuzend 68006 ▁okuba 68004 ▁soad 68004 ▁technika 68004 نګه 68000 ▁Distrito 68000 ▁директно 67999 ▁اچڻ 67998 ▁ភ្ល 67997 olution 67995 ığında 67995 ▁راډ 67995 ▁aiko 67994 гради 67987 ▁gördü 67987 ▁musia 67987 ▁хэрэгж 67987 ▁SEC 67986 ▁accesso 67985 ▁мнению 67984 ▁сүр 67984 danning 67983 ▁mux 67983 ▁պատգամավոր 67983 ▁وید 67983 ▁Schwe 67981 میز 67980 ▁positivo 67978 ▁ਵਿਆ 67978 ▁aldrei 67976 ▁sukces 67976 ▁Siria 67975 ▁xung 67975 ▁Estonia 67974 ▁diccionari 67974 attom 67972 ▁bào 67972 ▁இற 67972 ▁tracta 67970 ไหว 67969 روم 67968 ▁თანამშრომ 67968 ▁የኦሮ 67968 インターネット 67966 סף 67965 ▁изключително 67963 ycznym 67962 ▁ജെ 67962 아이 67962 jug 67959 ▁Weise 67959 ▁reunión 67958 ▁दिवा 67958 ▁වය 67957 umana 67956 ▁altă 67956 ităţii 67955 ▁grat 67955 ▁سلو 67954 ▁लड़की 67954 今後 67954 ▁volk 67953 ajad 67952 ếng 67951 ▁білу 67951 ▁troj 67950 簡単に 67950 ភិវឌ្ឍ 67949 ▁ጥቅም 67948 ▁vezañ 67947 ▁српског 67947 քերի 67944 ▁මග 67943 ਪੰ 67940 ▁фонда 67940 isje 67939 ▁amianan 67939 ▁실시 67939 ziť 67938 ▁Kuz 67938 ▁Lenn 67938 ▁мөрәжәғәт 67938 ▁ଛାତ୍ର 67937 тог 67936 ▁gejala 67936 ▁maqsad 67936 ▁хва 67935 ាជ្ញ 67934 ខ្ល 67933 elua 67932 ▁naturlig 67932 ▁विद्यु 67932 ▁dağı 67931 ▁Эх 67931 चक 67930 asiswa 67929 ເພີ່ມ 67929 cb 67928 говоре 67928 ▁kuiv 67928 kru 67925 VIII 67924 ச்சை 67924 ▁destes 67923 ▁đột 67922 ▁lalàna 67921 ▁الكريم 67921 gató 67919 ▁,‬ 67919 ▁Qora 67919 ▁образо 67918 纯 67918 aný 67917 ţine 67917 ▁deli 67915 ▁այցել 67915 ▁Mohammed 67914 ▁правят 67914 ▁بزن 67914 ▁Programmer 67912 ▁θέματα 67912 ▁sintomas 67911 ▁teeb 67911 ▁wyst 67910 ▁Ür 67909 ▁აშშ 67908 nutia 67907 ▁기존 67903 ▁මෙන්ම 67901 масы 67900 ▁gagge 67900 ▁ليست 67900 ੈਂਟ 67899 ▁Gaba 67899 ▁सार 67899 upra 67893 ▁gió 67893 ▁سد 67893 ▁არსებობს 67893 bew 67892 ▁Aplikasi 67892 ▁historien 67892 jall 67891 ▁сентяб 67891 ▁নির্বাচ 67889 รีเม 67888 ▁medo 67888 ▁טראמפ 67888 लेट 67887 ▁pH 67887 ▁parar 67887 essuu 67886 ▁adapta 67881 ▁होंगे 67881 esaist 67880 obrīd 67879 siyada 67879 ▁ನಾಗ 67878 ნიერ 67876 ρωσης 67875 әсәт 67875 ماني 67875 ▁kilpail 67875 ▁skrb 67875 :8 67874 ▁commercial 67874 meta 67873 ▁заштита 67872 ▁заходів 67871 ▁اکت 67871 ▁भ्र 67870 ▁hommes 67869 сд 67868 ▁olym 67868 ですから 67868 ረም 67867 ▁hoàng 67866 ▁ժ 67866 ▁왕 67866 ööd 67865 ▁NV 67864 ▁demor 67864 ▁ولس 67864 ▁Liste 67863 ▁loja 67863 製造 67863 wụkwọ 67861 दीप 67861 ということで 67861 هاء 67860 ODA 67858 りを 67857 ▁ladawan 67856 ▁nasil 67856 load 67854 jät 67853 istiwa 67851 ▁ક્યાં 67851 ▁듯 67851 ▁საუკუნ 67850 ▁retten 67849 យោ 67848 ▁تلویزیون 67848 ປາ 67847 ▁ism 67847 ▁змен 67846 elic 67845 ▁Baw 67845 ስን 67843 ▁میشود 67843 наас 67841 ▁wrinkles 67841 лектро 67840 لیف 67839 ▁сказала 67839 ତ୍ମ 67838 施工 67838 美的 67838 orsz 67837 ቧ 67837 กล้อง 67836 ▁apsaugos 67836 ▁govèrn 67836 ▁rəhbəri 67836 ▁sürdür 67836 elsens 67835 utam 67832 ▁VAN 67832 ▁ಸದಸ್ಯ 67832 ံဳ 67831 ▁thí 67830 ▁militares 67829 ▁सध्या 67827 ولوژی 67826 ▁وصول 67826 ▁ধর 67826 ▁කරගෙන 67826 ▁Alamat 67821 TION 67820 agsak 67819 бени 67819 ▁думи 67818 ▁тен 67818 생활 67818 db 67816 ▁pojawi 67816 ▁فترة 67816 の方は 67816 בעי 67815 ▁oriental 67815 ▁برتر 67814 ▁Phụ 67813 ▁Tuo 67813 alism 67811 Ҙ 67811 ▁allá 67811 ▁културе 67811 ▁Spika 67810 忠 67810 ▁sowohl 67809 laste 67807 ▁ισχύ 67806 ▁keben 67805 ▁יוני 67805 ▁thầy 67804 ▁застосову 67804 alisis 67803 ▁поране 67802 ▁User 67800 ▁মূল 67798 ▁արդյունք 67796 ▁بف 67796 eser 67795 idheachadh 67793 ▁가능한 67793 antoor 67792 gesetz 67791 łoś 67790 ▁महारा 67789 ኮኖሚ 67788 ▁läpi 67788 ▁المك 67788 ▁направления 67787 ▁Đó 67786 ▁Ազգային 67786 ▁konstat 67785 ▁PIC 67783 ▁esin 67783 inkite 67781 ମରେ 67781 ▁Nada 67780 ▁ipar 67779 ▁βελ 67779 ▁китә 67777 ▁دى 67777 ▁പക 67777 ▁Diễn 67776 ▁gida 67776 ▁numeros 67776 ▁मैच 67775 ▁Wyb 67774 ▁empr 67772 ▁לפע 67772 ▁уред 67771 一部 67770 itset 67769 ▁ପୂର୍ବ 67769 ുകളുടെ 67767 ▁مسئله 67767 ▁Chiar 67766 íše 67765 ▁domat 67765 ▁Associa 67763 ▁تشخیص 67763 ▁სრული 67763 тинен 67762 ▁ေဒၚ 67761 ▁منف 67760 ▁80. 67759 ▁polov 67758 الر 67756 ▁Ganz 67756 ▁hrane 67756 ▁Nors 67755 ▁живи 67754 ▁പോയ 67754 henti 67752 ▁Robin 67751 ವರಿ 67750 ▁Comhrá 67750 ▁Silahkan 67750 ▁संसद 67750 ▁şəxsl 67748 ▁محترم 67748 ▁വിധ 67748 cino 67747 ▁رامنځ 67747 כטי 67744 aniiru 67743 ▁Agric 67742 ▁Shqipëria 67742 ▁прево 67742 важно 67741 ▁chiqish 67741 ▁efektif 67741 ▁минь 67741 ▁ಗಮನ 67741 ▁nyer 67740 ستانی 67739 testület 67738 ▁همدې 67738 ▁waayo 67737 ūdz 67736 ▁ସରକାରଙ୍କ 67736 ▁zev 67735 ▁uživat 67732 ▁отно 67731 ▁përgj 67730 ▁tumani 67730 теу 67728 ▁tanong 67728 ▁zabal 67728 ▁гър 67727 ▁прият 67727 ▁вяр 67726 holt 67725 ▁είμαστε 67725 ▁numar 67723 ▁світі 67723 ▁አለመ 67722 ▁▶ 67722 ສະແດງ 67721 ▁스타 67721 မည် 67720 ▁intelektual 67720 نع 67718 ▁tıkl 67718 ▁پیغام 67716 urgi 67715 ινά 67713 ▁succès 67713 क्ने 67711 njak 67710 ▁bejegyzés 67710 ▁търгов 67710 ▁شاد 67710 ▁ಕ್ಯಾ 67710 ▁ilus 67709 ▁necha 67709 ▁صندوق 67707 ттеу 67706 ▁základe 67705 ▁प्रस्तुत 67705 ▁będ 67703 abilidade 67700 ▁katerega 67700 xxx 67699 ▁sõid 67699 ▁Tent 67698 тю 67697 ▁gamma 67695 ▁meur 67695 ▁məşğul 67695 企业的 67695 aransa 67694 ▁வீடியோ 67694 ▁भवन 67692 ▁entwéck 67691 ▁iem 67690 ▁masukkan 67690 ▁پاڼې 67690 ▁මාව 67689 ▁awit 67688 лює 67684 ▁Kiek 67684 ▁poznat 67684 pravu 67683 gabe 67682 ▁safar 67682 審 67682 ▁حڪم 67681 евка 67680 ▁Educação 67680 ▁kehit 67680 ▁Apost 67679 ▁spännande 67679 ▁Suu 67677 ▁Maya 67674 ▁والج 67674 りが 67674 ▁പക്ഷേ 67673 ▁פלי 67672 คอ 67670 ▁uY 67670 هنگ 67668 ມູນ 67668 īgās 67667 ▁Emin 67667 ▁parod 67667 uzzle 67665 ▁Okul 67665 ▁deshalb 67665 ▁дает 67665 たちが 67664 USK 67663 ▁onga 67662 ▁kelion 67661 дарҙа 67660 ▁નીચે 67657 ใ 67655 ▁अर्ज 67655 тесь 67654 िकी 67654 ▁Ван 67653 洁 67653 ▁llega 67652 ▁ունեցող 67652 ▁substan 67651 ▁Edge 67649 ▁앞으로 67646 ▁nòstra 67645 ▁במס 67645 ▁wurin 67641 董事 67641 ▁batas 67640 ▁hoge 67639 ▁ngicela 67639 ▁yaada 67639 فاظ 67638 ▁väik 67638 ▁sıcak 67637 ေဘ 67636 නික 67635 ▁tù 67634 ▁kongres 67633 olaa 67632 ▁Gobiernu 67632 ▁fasilitas 67631 amanje 67629 ▁ବିଶ 67629 rụ 67627 ασία 67626 ▁ಅದನ್ನು 67626 ▁Гали 67623 ุย 67621 ▁Kombin 67621 ▁inimeste 67621 ▁Musi 67620 ▁husus 67620 ▁որպեսզի 67620 ▁estats 67619 ▁respeito 67619 ▁đu 67619 selig 67618 ▁παιχνίδι 67618 ▁asg 67616 wécklung 67615 ▁autón 67615 ▁productes 67615 ρίζουν 67614 үмәт 67610 രുന്നത് 67610 完整 67609 ▁illik 67608 ▁יעקב 67608 ▁340 67605 ▁Sabtu 67605 ▁યોગ 67605 hathal 67602 ▁материала 67602 entligt 67601 cert 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67469 ▁ημέρες 67469 ▁analiza 67467 ivon 67466 ամեն 67465 ▁Også 67465 ▁gamm 67464 ▁Лондон 67464 ▁pina 67462 ▁zaterdag 67460 adair 67459 ▁"" 67459 nous 67457 ▁técnicos 67456 ▁vete 67456 ତିକ 67455 ▁anba 67455 ▁créd 67455 äänud 67452 јска 67452 ԵԼ 67452 ▁Chem 67452 ▁தொடர்ப 67452 agiti 67451 ▁173 67451 ▁tregon 67451 ▁wierklech 67450 ▁ភូមិ 67448 ▁गोल 67447 ▁წარმატ 67445 ▁elit 67443 ▁مڪمل 67443 ▁РБ 67442 ÂN 67441 ▁struk 67441 ▁प्रचण्ड 67440 ▁Syarikat 67438 ▁للبيع 67438 빠 67438 ▁ទាក់ទង 67437 ювання 67436 ▁Vorte 67435 悉 67435 ▁effe 67433 ▁гульні 67432 わからない 67431 ▁француз 67430 alanga 67429 ၏။ 67429 ▁nátt 67429 ▁Стара 67429 Cla 67427 ▁recenz 67427 ▁ब्या 67427 ▁ól 67425 ▁општина 67425 ▁широко 67424 nı 67422 ▁Fotograf 67422 ▁kokos 67422 ▁Андрей 67422 ▁Today 67420 cısı 67419 dorf 67419 ▁haig 67419 ▁italiani 67419 ▁Kültür 67418 ▁spieg 67418 ▁împotriva 67418 ▁αντιμετω 67418 ▁демо 67416 ▁Zona 67415 ommal 67414 weeg 67414 ▁büt 67414 ▁neist 67414 ▁ят 67414 凯 67414 ▁halua 67412 ▁mengi 67412 เรียนรู้ 67411 ▁ತಂತ್ರಜ್ಞಾನ 67409 ကုန္ 67406 ▁kisha 67406 ▁διαδικ 67406 नको 67405 ▁місті 67405 ▁teuing 67404 ▁dzied 67401 ▁линии 67400 ▁nagusi 67399 .[2 67398 ▁вина 67397 posició 67396 prez 67395 ▁pantai 67394 ▁विवरण 67394 ▁lahend 67393 ▁seama 67390 ▁غا 67390 ▁Неко 67389 iladigan 67386 ▁atta 67386 央 67386 mission 67385 ▁eser 67385 лакти 67382 спек 67382 ▁thước 67380 ▁birt 67379 ▁utiliser 67379 ▁זאגט 67379 ▁ประเทศ 67379 ▁اتر 67377 wh 67376 ▁பெயர் 67376 ▁районының 67373 ▁Vaikka 67372 ▁jee 67372 ▁kaff 67372 ▁preživ 67372 ▁عبور 67372 וסיף 67370 ▁ahir 67370 ▁présente 67370 ▁scorso 67370 MC 67369 ▁ഥ 67368 ▁እንግ 67367 সা 67364 ▁барлыҡ 67363 ▁السيا 67363 ▁introd 67362 ▁որն 67362 ▁lakay 67361 ▁fars 67360 ▁ljus 67359 ▁precip 67359 ہن 67357 ▁удоволь 67357 ▁先 67357 ▁olahraga 67356 ▁ifanc 67355 ▁jedes 67355 שרד 67354 ▁elkész 67354 ІІ 67353 ▁listaan 67353 থে 67352 attività 67351 ຣະ 67350 ຽມ 67350 Cro 67349 ▁ट्रा 67349 ▁стоимости 67348 wyddiadau 67347 ▁Forest 67347 ▁маркетин 67346 جيب 67345 ▁trase 67344 ਾਨਕ 67343 ಕೆಯ 67342 ታል፡፡ 67342 ▁lajan 67342 ▁presión 67341 ▁nærmere 67340 wong 67339 ▁Juga 67339 chant 67338 ▁Dá 67338 ▁cinci 67338 atici 67337 ▁abat 67337 ▁cəlb 67337 larınız 67336 യോഗ 67336 ▁grå 67334 ▁душе 67334 ▁povrat 67333 ▁تحصیل 67331 センター 67331 ๑ 67329 ▁Lëscht 67329 ▁politiet 67329 ▁Title 67328 ▁육 67327 ▁कीमत 67326 自治 67326 인을 67326 ▁посилання 67325 żd 67324 רונים 67324 ▁Bēr 67324 ▁posicion 67324 ▁طريقة 67324 ම්ම 67322 ▁своята 67322 ▁અવ 67321 െയുള്ള 67318 ▁කියන්නෙ 67318 ▁KN 67317 ▁contenido 67315 igues 67313 ceae 67310 ▁pasako 67310 ▁Frit 67309 ▁nokuz 67309 ▁ezalaki 67306 ▁mussen 67305 SU 67303 ▁لیت 67303 ketak 67302 ▁vê 67302 ▁Italya 67300 ▁gær 67298 ▁paslaugos 67298 amore 67297 ▁franséisch 67297 ▁vajalik 67297 äste 67295 ▁Beim 67295 ulungiswa 67293 ▁краще 67293 ▁ακού 67292 ősége 67291 ▁evela 67291 ▁głos 67290 ▁Еги 67290 ▁நிக 67290 讨论 67288 ▁formació 67287 ▁varandra 67287 wiedz 67285 ▁Satan 67285 ▁elim 67285 依頼 67285 ▁štú 67283 ▁стосу 67283 eders 67281 ដ្ឋ 67281 ▁препоръ 67281 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рине 67223 בוי 67223 YON 67222 otų 67222 ▁perzh 67222 ▁Επίσης 67222 abord 67221 ▁बुध 67220 ▁ისეთი 67220 техни 67218 ▁inteligen 67218 ▁nust 67218 ▁diario 67217 னின் 67216 ▁uhlelo 67215 ilayotgan 67213 lager 67213 ostęp 67213 ▁රීඩා 67211 ▁дороги 67210 ସିଆ 67209 ▁prind 67209 uwd 67208 ▁kişinin 67206 ▁połąc 67205 ▁vrhun 67205 arbeitet 67201 ▁nyere 67201 ▁ଅନୁସ 67201 ritt 67200 ▁tarj 67200 ▁kiezen 67197 ▁reproduk 67197 ثمار 67196 ▁esigenze 67196 ▁persoana 67196 ▁неї 67196 àinig 67193 ćenje 67193 ılım 67193 ▁ankehitriny 67193 ▁lampu 67192 ywood 67191 ▁հաջող 67189 ტიტუ 67188 ▁tròp 67188 ▁Bongo 67187 ဖူး 67186 ОС 67185 ▁سرویس 67185 アメリカ 67185 ▁сонгуу 67184 ▁រថយន្ត 67184 kő 67183 քերը 67182 лії 67181 ▁தேசிய 67181 ▁Jensen 67180 ▁castel 67179 ▁vrouwen 67179 ფორ 67177 ▁ghế 67177 ▁Dinas 67174 ▁квітня 67173 ▁sarak 67171 ▁trece 67171 年間 67170 estellt 67168 ▁afric 67167 ินทร์ 67166 ▁domanda 67166 អនុ 67165 ▁▷ 67165 વાના 67164 ላጊ 67163 ▁teatru 67163 之间的 67163 ▁Կայ 67162 ▁kvadrat 67160 ▁veiksm 67159 ▁உற 67158 osius 67156 ▁pomen 67156 iriz 67155 ▁समी 67155 ouert 67152 ಿಕೊಂಡು 67151 ▁izvēlēties 67151 ▁জীবন 67151 ▁lehető 67149 ссер 67148 ▁crédito 67146 ዳንት 67145 ellung 67144 ▁Bron 67144 hésitez 67143 ▁gerekli 67143 ▁muuj 67142 стә 67141 נין 67140 ▁hjälper 67140 ▁itz 67140 ▁ünvan 67139 ▁rexistr 67138 ▁lugu 67137 ▁അറിയാ 67137 ▁hebat 67136 ▁წინააღმდეგ 67136 okos 67134 ▁stronę 67134 ertet 67129 ผสม 67129 ▁нақты 67129 스로 67129 ▁जवाब 67127 ▁chambre 67125 ▁جګړه 67125 ▁NJ 67124 ▁rửa 67123 urte 67122 ▁nataon 67122 ▁skad 67121 ▁wedst 67121 ▁verksam 67120 nere 67119 ▁Kuf 67119 ▁крыні 67119 ▁विकेट 67118 ▁teko 67117 דיו 67115 ▁ስን 67114 ▁당시 67114 ▁devenit 67113 ▁wers 67113 ▁ባህ 67113 财政 67113 ನಲ್ಲಿ 67110 ▁دهشتگرد 67110 ▁зні 67108 ▁ໄລ 67108 јева 67104 เสมอ 67104 wethu 67103 ▁хариуцлага 67103 ▁ବିଜେ 67103 ▁Masih 67102 沈 67101 ابعة 67100 действия 67099 ▁jóvenes 67096 roż 67095 teraka 67095 ▁jardin 67095 əndə 67094 ▁torpaq 67094 ▁účinn 67093 ▁akwai 67092 ▁zvlád 67091 ▁človeka 67091 ▁18+ 67089 ネル 67089 ▁ইং 67088 ▁друзь 67087 70, 67086 ▁giảng 67085 xta 67084 əbb 67081 Ầ 67081 ▁ganas 67081 ▁տնօրեն 67081 ▁quarto 67080 rub 67079 ლიდან 67078 ▁dobrý 67078 ▁уран 67078 ▁Nges 67076 ▁člov 67075 vrt 67074 ▁шаруа 67074 ▁ਕੋਲ 67074 ▁ଜା 67074 ▁Инг 67073 周年 67071 युक्त 67070 ▁швидко 67070 ▁koha 67067 atawa 67066 ▁člán 67066 ▁Pw 67065 ▁nég 67065 ▁bèt 67064 ස්ථ 67060 ▁കൊല്ല 67060 ▁Origin 67058 ▁aandacht 67057 ▁italian 67056 ▁ѝ 67056 ហេតុ 67055 ▁vakantie 67055 ▁zīm 67055 ▁ফেসব 67054 ▁morm 67053 ▁побо 67053 ຊາຍ 67052 ▁inzer 67052 ▁विधा 67052 ▁selleks 67051 arteko 67049 cli 67049 ընդ 67049 ▁susid 67047 ▁gina 67046 ▁Paula 67045 ▁Yks 67045 ▁гэтыя 67042 ▁उसने 67042 ikkan 67041 ▁maraqlı 67041 ยังไม่ 67040 ▁etgan 67040 ▁חר 67040 influ 67037 ່ວຍ 67037 ▁MATOKEO 67037 ▁bež 67037 ▁수준 67036 ▁Dėl 67035 ▁мэндийн 67035 ▁அளி 67034 ▁ಎಚ್ 67034 ▁himo 67033 ▁HABARI 67032 ▁прекрасно 67032 ▁ਜਾਰੀ 67031 ляе 67030 อันดับ 67028 ▁बस्ने 67028 etso 67027 ▁47. 67027 ÉG 67026 ared 67025 þjón 67025 ▁କରିଥିବା 67023 anyakan 67022 ▁психологи 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▁карактери 66205 ▁umth 66204 ▁यांची 66204 ▁Qaranka 66203 ▁împreună 66203 ▁ඉන්නවා 66203 elerini 66199 gili 66199 nten 66197 ▁смотреть 66196 ▁ukuq 66195 ▁Kadın 66194 ▁Lihat 66194 ▁morda 66194 ▁arro 66193 ▁názor 66193 ▁вій 66193 ▁Province 66192 ▁Γιώργ 66192 hih 66190 tapi 66190 ಿಸಲ 66190 ▁ulteri 66188 ▁19.00 66187 ▁بیٹ 66187 ▁வரி 66187 niú 66186 ავშირ 66186 ▁нари 66186 ▁neno 66185 ▁Elm 66183 ▁Họ 66182 forening 66181 udir 66181 ▁Йәш 66181 ▁respecte 66179 ▁Erfahrungen 66178 ▁سمجه 66178 erimine 66177 ▁yara 66177 siet 66176 नुसार 66176 15] 66173 ▁išsk 66173 ▁دليل 66173 ड़क 66172 黎 66172 циа 66170 ▁Diet 66170 ▁gezien 66170 ▁sezonu 66169 ▁vc 66169 ▁masy 66168 ▁muassas 66167 शाख 66166 ▁فضا 66166 ▁tutorial 66165 ẩy 66164 ▁ഓഫ 66164 årig 66162 ลึก 66162 ▁экологи 66161 ▁آشنا 66161 ▁능 66161 ကန္ 66160 літ 66159 ▁Sean 66158 ▁maty 66158 ▁მუს 66158 άρια 66157 ▁-10 66157 ▁images 66157 ▁дұрыс 66157 ▁emol 66156 ▁végén 66156 ▁អង្គ 66156 ▁ráno 66155 wè 66154 ▁ជ្រ 66152 NIK 66151 ěstí 66151 ▁втор 66151 ▁לחץ 66151 裕 66151 ÜN 66147 ▁feina 66146 ▁junior 66146 enje 66145 ▁demokrati 66145 ikot 66143 qir 66143 ▁شكل 66142 ▁என்பதை 66142 ▁סוג 66141 ▁መሪ 66141 ▁savjet 66139 ▁сканда 66139 adku 66137 ▁temsil 66137 ▁xusus 66137 ▁ազդ 66136 íž 66135 ▁analyse 66135 ▁qator 66135 ▁tuyên 66135 ▁ਖਾ 66135 mayın 66134 ▁Pilipino 66134 flex 66133 мағында 66133 ▁refle 66132 ▁مجازی 66132 ມີຄວາມ 66131 ▁aucune 66131 udesta 66130 ▁illərdə 66129 ▁paciente 66128 ▁गोपनीय 66127 機會 66127 ପୂର୍ଣ୍ଣ 66124 ▁ước 66123 ▁lược 66122 діру 66121 ▁መንግሥ 66121 ▁цркве 66119 ▁atsir 66118 fois 66117 ▁hoan 66115 !: 66114 ▁Pérez 66114 ▁keyf 66114 ▁গুল 66114 βαι 66113 ▁jeżeli 66112 าช 66111 ▁سیاسي 66111 ▁kirjan 66110 équipe 66109 ▁ಪ್ರೇ 66108 britannien 66107 emaña 66107 équ 66106 ωθεί 66106 ▁nenhum 66106 ▁хүчин 66105 ▁ஃ 66103 ▁сторон 66101 cuèlh 66099 iné 66098 raż 66098 atzaile 66095 jejo 66095 teki 66095 ิ่น 66095 inke 66094 ▁pārstāv 66093 ▁բառ 66093 ▁folg 66091 ▁மாநில 66088 ▁adultos 66086 ▁kawaida 66085 ▁බොහොම 66085 inzani 66084 ndal 66084 ▁Doctor 66084 ▁sætte 66084 ▁váll 66082 ▁VV 66081 ▁даргын 66081 ▁ovoga 66079 бітни 66078 ▁เส้น 66078 DER 66075 тельством 66074 ▁өлөшләтә 66074 ▁cháu 66073 ▁διάφο 66073 ▁heula 66072 ▁Голов 66072 ▁boas 66071 ▁ngazo 66071 ▁тялото 66071 ▁Adat 66070 ▁évvel 66066 ▁конкурсу 66066 ▁новой 66066 ▁edició 66065 ▁эшен 66065 ▁اقوام 66065 एफ 66063 ▁процедура 66063 ▁ඈ 66062 ▁Style 66061 علوم 66059 າທິບໍດີ 66059 ▁mota 66058 缩 66058 ▁የባ 66057 ukuum 66055 ▁Mbunge 66055 ▁знако 66053 ▁խոր 66053 ▁المصرية 66052 ショップ 66052 ▁durumda 66051 ▁задъл 66051 ▁عہد 66051 ▁подраз 66050 ▁рэа 66049 Vol 66048 Das 66047 vård 66047 ▁Viena 66047 ▁magasin 66047 ▁čokol 66047 канамі 66045 зяць 66044 wort 66043 ▁өйрәне 66042 ▁disple 66041 یدر 66040 ▁económ 66040 ▁positiiv 66040 ▁indem 66039 festa 66038 ftaran 66037 ulare 66037 ▁bumili 66036 ▁व्यवसा 66036 ▁ሸ 66035 ▁karibu 66033 ▁худалдаа 66033 ▁একটা 66033 лаж 66032 स्कृ 66032 ▁kapcsolatban 66031 dios 66030 ▁thoải 66030 ▁elip 66029 ไน 66028 ▁tabel 66027 ਵਲ 66026 ίκα 66024 ▁nadz 66023 ▁(18+), 66022 ▁viesnīcas 66022 ▁атай 66022 ▁Фин 66021 ▁الحصول 66021 seo 66020 امہ 66020 ▁obla 66019 byt 66018 ▁uniform 66017 ▁імені 66017 ΤΗ 66016 ▁dhashay 66016 ▁labarai 66016 ▁řekl 66015 ▁sawa 66014 ▁дото 66014 ▁지방 66013 ernu 66012 find 66012 ▁Nelson 66012 AVA 66011 හෙන 66011 ekisi 66010 utzt 66010 віа 66010 പ്പുറ 66010 ▁Predstav 66010 ▁verbess 66010 သမား 66008 народна 66005 يام 66005 gwọ 66004 ljivost 66004 ▁Palvel 66004 gjort 66003 ិរ 66003 ▁Savien 66003 ческите 66002 ▁Firm 66002 elto 66000 əşdir 66000 000, 65996 ▁colon 65995 ઓની 65994 ▁ລົດ 65994 先进 65993 ▁koske 65991 áltam 65990 ▁เขา 65990 ▁menjual 65988 แน่ 65987 ▁didžia 65987 ▁videá 65987 պր 65984 jọba 65982 ▁Побе 65981 ▁퍼 65979 ▁ostali 65978 Benz 65977 سبة 65977 ▁moći 65977 ▁កំណ 65976 pred 65975 ▁golpe 65975 agot 65973 ▁disfr 65972 ▁२८ 65972 ▁ਰਹਿੰ 65971 ambung 65970 emor 65970 jørn 65970 ▁मोबाईल 65970 ▁Unterstützung 65969 iders 65968 ▁빨 65968 ▁Desta 65967 시설 65963 ▁mustah 65962 ▁дейді 65961 ▁наво 65961 woners 65960 แม้ 65959 ▁jezika 65959 imassa 65958 δου 65958 ▁ujenzi 65958 ▁svojoj 65957 asir 65956 jenester 65956 ▁látt 65956 ▁आवाज 65956 were 65955 ▁sprog 65955 дуть 65954 ▁horret 65954 RET 65953 ▁मिली 65953 ▁minuut 65952 leich 65949 γες 65949 ▁denuncia 65946 ▁рису 65946 ▁уйын 65946 байыш 65945 মিক 65945 ▁Михаил 65943 ▁ktorým 65942 äht 65941 ってきた 65940 angkeun 65937 One 65935 ▁antigu 65935 ▁וז 65935 ▁जेल 65935 ▁можливо 65934 Ờ 65933 ▁Interview 65933 ▁Жер 65933 ▁чемпи 65933 ▁للق 65933 ▁Gjith 65932 ▁राजस्थान 65932 ▁Mesa 65931 kari 65930 гать 65929 ▁intensiv 65929 ▁ОП 65929 ▁симпто 65928 ugod 65927 ▁בגלל 65927 inggal 65925 ▁asar 65925 ▁žmogaus 65925 ayos 65924 hibi 65924 ▁halu 65921 ለጠ 65920 ຄານ 65918 elekea 65917 نوان 65916 зад 65914 ଶୁ 65914 ▁eläm 65912 ▁پې 65910 廳 65910 وء 65909 ၂၀၁ 65909 ▁incont 65908 ▁Twe 65907 ▁მეფ 65907 ▁kazao 65906 ಡಿಯೋ 65905 ▁गठन 65905 iseks 65903 ▁الجمه 65898 ▁ເຈ 65898 ▁იყ 65898 ▁contrata 65897 ▁أد 65894 ▁कठ 65894 एक 65893 ▁मृ 65893 ிட 65891 ▁niveles 65891 ▁کانال 65891 tsaina 65889 ▁ನಿಮಗೆ 65889 ujúce 65888 ▁bulak 65888 ▁поси 65887 ▁ಸಲ 65887 ydan 65886 ذة 65886 etts 65885 ▁vispār 65885 ionar 65884 ▁Дума 65884 ▁стратегия 65884 posto 65883 วิจัย 65882 بند 65881 ▁Klaipėdos 65881 ▁laha 65881 ▁dolazi 65880 ▁oga 65879 ВІ 65878 ▁ڪپ 65877 かけて 65877 녹 65877 isid 65876 ▁HCM 65875 ▁rëndësishme 65874 ▁Eest 65871 ▁prepre 65871 ▁строительства 65870 ▁تحمل 65870 токот 65867 ମ୍ପ 65867 vimo 65866 ▁zapom 65865 ▁рын 65865 egin 65864 ▁වසරේ 65864 સમાં 65863 ይይት 65863 ▁შესაძ 65863 能夠 65863 ▁Kritik 65862 ▁esibhedlela 65861 ▁בעת 65861 ▁اداک 65858 ▁такую 65857 ▁нарын 65855 דרי 65853 ▁favori 65852 ▁Ciudad 65851 телю 65850 स्तान 65849 ▁прызна 65845 ረብ 65843 ▁õpil 65841 ▁öğ 65841 ▁ไม่ว่าจะ 65841 کاری 65840 ▁KUR 65840 ▁mouv 65837 ▁llengua 65836 ▁специалистов 65836 ...: 65835 लोक 65833 ▁πολί 65833 ksomhet 65832 ▁ederim 65832 ▁̈ 65831 ▁καλό 65831 ▁विवाह 65831 ▁язык 65827 ▁ເຫັນ 65826 סות 65823 க்கை 65822 ▁tykk 65820 ▁aseg 65819 ▁xodim 65819 МС 65817 ▁raba 65817 と思っています 65817 公安 65817 烦 65817 ▁Ҡыҙыл 65816 ▁ایست 65816 ▁لیست 65816 മുണ്ട 65814 ▁Bukit 65814 ▁gisa 65813 ▁станда 65813 ▁Osim 65812 ▁სამუშაო 65811 ▁aydın 65810 ázs 65809 digt 65807 aquò 65806 компа 65806 ேயே 65806 ▁izobražev 65806 ▁kiinni 65806 ▁דעמ 65806 大きく 65806 롤 65806 ಾನಿ 65805 ampy 65803 ▁prywat 65803 ▁كثير 65803 ▁बोला 65803 ▁престо 65801 밥 65800 ո՞ւ 65799 ▁судеб 65799 ▁шек 65799 ▁בתק 65799 itivo 65798 tabl 65797 ▁bevis 65797 ▁Ronald 65796 ▁विदेशी 65796 ▁Kitu 65793 ▁Poss 65792 ▁operativ 65792 ▁வரலா 65791 kaid 65790 orgen 65788 ▁rach 65786 ▁lafiya 65785 naði 65783 ▁2000- 65779 ▁හැබැයි 65778 ▁filho 65777 ▁अॅ 65777 ▁ഭാര്യ 65776 teo 65775 ▁самы 65774 特徴 65774 ▁Sne 65773 ▁басқарма 65773 ▁goreng 65772 Abstract 65771 ուբ 65771 ▁буда 65771 ▁рәүештә 65771 lati 65770 турата 65770 ▁banco 65770 ▁عليهم 65769 ▁මෝ 65767 ▁ნაწილი 65767 ▁Ink 65766 ▁konsekvens 65766 μιση 65764 ходять 65764 ▁representación 65761 ▁තේරු 65761 ▁Arabia 65760 ▁Eri 65760 ▁vises 65759 140 65758 ▁mehrere 65758 ▁rius 65757 ▁מצב 65757 ▁Marko 65756 ▁ماڻ 65756 ▁Antoni 65755 ೮ 65754 ▁båda 65753 ▁daň 65753 ▁)) 65752 euros 65751 fers 65751 ▁yima 65751 ▁অনলাইন 65751 ▁parada 65749 เครื่องบิน 65748 ▁Fis 65748 ▁volana 65748 ▁വായന 65748 био 65747 τισμό 65746 ສັງຄົມ 65746 ▁pričak 65746 പ്പെടുന്ന 65744 ઝા 65742 engqondo 65741 likult 65741 ndum 65741 ▁wyl 65740 ▁ერთმან 65740 ▁Krak 65738 spi 65737 séget 65736 zdu 65734 ронғо 65734 ▁научи 65734 ▁tänker 65733 積極 65733 गायत 65730 ▁Morena 65730 ▁meleg 65730 ▁uko 65730 ▁такий 65730 ▁Merk 65729 ▁Ocak 65729 ੰਮ 65728 ▁СДС 65728 েরা 65727 ▁регла 65726 ▁umumnya 65725 éné 65724 രന് 65724 ▁статье 65723 ▁منح 65722 ▁სტუმ 65721 ▁izmantojot 65720 ▁ирэх 65719 ņus 65718 nului 65717 ători 65717 ▁Клу 65717 ▁साव 65717 ▁പട്ട 65717 ೈಲ್ 65715 ịn 65714 лімпі 65713 เกษตร 65712 挙 65712 ▁sjov 65711 ▁ક્યારે 65711 ΩΣ 65709 ▁Solid 65709 ▁이메일 65709 embl 65708 prov 65708 ▁rencontr 65708 ▁amerikanske 65706 ▁cereb 65704 gawa 65703 家人 65703 বায় 65699 ▁Kerk 65699 ▁ardhur 65699 ▁δείχν 65698 ▁həsr 65696 ▁اہ 65696 ▁Vair 65695 ▁egitea 65695 ▁regen 65694 ICK 65692 amuun 65692 ▁etməyə 65692 onov 65691 ▁khổ 65690 ianatra 65689 بیل 65689 ▁товара 65689 ▁pě 65687 ▁kentañ 65686 ▁shqiptarë 65685 ▁Cole 65684 ▁prib 65683 ▁vendosur 65683 ▁yapı 65683 英文 65683 ੈਂਡ 65682 ធំ 65682 ស្ន 65678 ▁kesi 65678 ▁свети 65677 ▁inilah 65674 ovih 65673 sada 65673 дзіць 65673 ▁aproximadamente 65673 np 65671 tons 65671 usió 65671 ضاع 65671 ारो 65671 uluzi 65670 ▁Muut 65670 ▁pakalpojumu 65670 ▁služben 65670 ▁පළාත් 65670 ▁Hyvä 65667 ▁kishi 65666 plysninger 65665 ▁rôznych 65665 ▁ስብ 65665 uthatha 65664 ▁হিসেবে 65664 ynä 65663 ▁ել 65663 ▁публіка 65662 ▁שהת 65661 ölj 65659 ▁مسؤول 65659 ---------------- 65658 ផង 65658 ▁سرم 65658 ረጃ 65657 ▁прибу 65657 ▁risol 65656 ▁zemlj 65656 ▁Vlade 65655 ▁escala 65655 ▁אודות 65655 ▁რაოდენ 65655 ▁pàgina 65653 ▁đoán 65653 ▁супро 65653 ▁שהיה 65653 ▁saira 65652 ▁cháy 65651 ردار 65650 ทันที 65650 ারিত 65649 ▁عقب 65649 ▁ባን 65649 Od 65647 áilte 65647 ▁mandag 65647 ▁اذا 65647 ▁adib 65645 ▁ഭക്ഷ 65645 ليز 65644 ▁foll 65644 ▁garante 65643 ▁Иако 65643 ▁dodáv 65642 ▁ស្លាប់ 65641 јског 65639 ▁ido 65639 ▁SMA 65638 ▁אויפן 65637 වුන් 65633 ▁disput 65632 ▁Мекси 65632 ▁حافظ 65632 ▁पेट 65632 utud 65630 атр 65630 ▁ផង 65630 ithandayo 65628 ਵਰੀ 65626 ▁mop 65626 ▁radion 65626 ▁bezpečnost 65624 ▁жет 65623 ▁leida 65622 ▁DPR 65621 ▁páginas 65621 ▁відпо 65621 ▁партне 65621 ▁කෙන 65621 ▁podm 65620 ▁دائما 65619 ▁Regioun 65618 ▁mūz 65615 ▁uzysk 65615 ▁ವರ್ 65615 英语 65615 etakse 65614 heem 65614 ▁personali 65614 ▁Тү 65614 ólk 65613 ▁megk 65613 ALAN 65612 ▁• 65611 ▁вопросам 65611 搜 65609 统一 65608 ▁Josef 65606 ahanan 65605 ିପ 65605 ▁kebanyakan 65604 ▁ટકા 65604 ▁nòstres 65603 ▁اذ 65603 чною 65602 ▁ابتد 65600 ▁ডেস্ক 65599 ERG 65598 atsiooni 65598 ▁ethi 65598 ทรวง 65596 ▁akit 65596 ▁mogla 65596 ▁Свето 65596 styrelsen 65595 ▁миллиард 65594 ▁Sarawak 65593 ▁atak 65593 ▁септем 65593 ▁حیر 65593 ▁họp 65592 ▁জানিয়ে 65592 ിക്കി 65591 ंटे 65590 ບາງ 65590 ▁бағалау 65590 ואה 65589 ▁respe 65589 ▁vägen 65589 ▁удалось 65588 ▁sheegtay 65586 ▁diss 65585 ▁elements 65585 ▁શી 65585 lir 65581 ▁нові 65581 ▁uru 65576 reno 65575 ətinin 65575 ▁прозо 65575 town 65574 ▁ಕಾಮ 65574 ▁bouw 65573 ▁įmonės 65571 ▁министр 65571 เศรษฐ 65570 ▁Trim 65569 ▁fewn 65569 ▁शिक्षण 65569 ▁さ 65569 ▁verbr 65568 inny 65565 τήθηκε 65565 їха 65565 ଧିକ 65565 ອຍ 65562 ▁Foc 65562 ðurinn 65561 မဲ့ 65561 ▁ਮੌਕੇ 65560 λευθε 65559 ▁kitą 65559 ▁zhr 65559 ▁Selepas 65556 ▁TAL 65556 ▁najväč 65555 ▁Dien 65553 ▁cuide 65553 ▁приклю 65552 ▁Stars 65551 ▁өдр 65551 ▁ናቸው፡፡ 65551 ڊيا 65550 ԱՎ 65548 ▁riesgo 65548 าระ 65546 ushi 65543 ▁koox 65542 ▁නෙවෙයි 65542 ▁fush 65541 ▁ბუნ 65541 ▁Jeigu 65540 dehibe 65539 λίας 65539 ministrazione 65538 hatnak 65537 တုိ႔ 65536 ▁რეგი 65536 ▁Ország 65535 ▁cittadini 65535 ▁množstvo 65535 ▁Міжнарод 65535 ālais 65534 ▁תנ 65533 ibilidad 65531 ▁Câu 65531 ▁ytterligare 65528 ዚሁ 65527 њој 65526 ▁1999, 65526 ▁ڪمپ 65526 ግጥ 65523 ▁aband 65523 ▁ermög 65523 ▁કરવાની 65521 ▁üb 65520 datert 65519 ▁érvény 65518 ▁̧ 65518 ▁Обе 65517 ▁припи 65517 ាសា 65516 ▁группа 65516 ▁institucional 65515 ▁poderia 65515 ▁visos 65514 ▁зур 65514 ▁яһау 65513 总是 65512 uksien 65511 chent 65510 ▁pember 65510 ▁טיפול 65510 ▁Bam 65508 ▁čisto 65508 ▁Kréyòl 65507 ▁ontmoet 65507 ဂၢ 65505 აციას 65505 ▁зал 65505 ▁خار 65504 ▁يونيورس 65504 ▁વિદ 65504 реса 65503 ▁אינה 65502 ▁Provinsi 65501 ▁perioden 65501 нээс 65500 ▁kiradi 65500 ▁навы 65500 ▁һауа 65498 osof 65497 ▁ସମ୍ପର୍କରେ 65497 ▁ukuthula 65496 ▁անվան 65496 teva 65494 ถว 65494 ▁Tamin 65493 ▁výbě 65492 ▁۲۵ 65492 ผู้หญิง 65488 哲 65488 ▁çı 65487 sn 65486 ▁Chir 65486 ▁accue 65486 ▁elik 65486 ▁inge 65486 นั่ง 65485 ▁Afya 65483 द्रा 65481 ▁mély 65481 ▁bakeng 65480 ▁شهادت 65480 ecutivo 65477 ernos 65477 honeg 65477 ▁rät 65477 ▁њега 65477 ▁ተብሎ 65476 nific 65474 անին 65474 ॉन 65474 ালিকা 65474 ▁würden 65474 ビジネ 65474 ziga 65472 քների 65472 ▁izle 65472 ▁változat 65470 장에서 65470 નિયા 65469 ▁analit 65468 ուրբ 65466 ມາຊິກ 65465 ▁Stabil 65465 чног 65464 ▁roue 65463 túnṣe 65462 órias 65462 ▁sóng 65462 delig 65461 ▁droits 65461 ▁اخلاق 65461 ▁مائ 65461 ▁उद्देश 65460 ▁gangen 65458 ▁तपा 65458 ូរស័ព្ទ 65457 imanapun 65456 taus 65456 ▁algúns 65456 ▁eze 65456 artner 65453 ረጋገ 65452 ▁denke 65452 ФОТО 65451 ածու 65451 ▁zet 65451 ▁मूल 65451 liyinə 65450 алы 65450 ଂଶ 65449 ▁otom 65449 ▁serien 65449 apac 65448 ▁Marx 65448 ▁ಪ್ರಸ 65448 ▁Essen 65446 ▁Igor 65443 ▁Cami 65442 ékony 65441 ▁ውሳኔ 65441 erre 65440 slē 65440 ▁מוכ 65440 лаштырыу 65439 ▁constat 65438 ▁täg 65438 ▁versione 65436 ▁největ 65435 ▁ຕົວ 65435 ▁folosit 65433 عتقد 65432 bered 65431 etal 65431 ▁ign 65431 ▁stov 65431 ▁סא 65431 ▁Fh 65430 ▁aproximativ 65430 ▁එදා 65430 ▁sög 65429 ▁ସମସ୍ୟା 65429 ▁ಕೇ 65428 aiga 65427 weka 65427 ▁centras 65427 şt 65426 ജു 65426 ▁مضبوط 65425 ▁Sandra 65424 estyle 65422 okoj 65420 ▁قضا 65420 ▁Evo 65419 ▁θέλει 65419 Organ 65418 дис 65418 ▁izmed 65418 ▁penerbangan 65417 रत 65416 ▁riigi 65416 antsika 65413 ▁Paj 65413 ▁երկրի 65411 kare 65408 ស្វ 65408 ▁Ith 65408 ▁людина 65408 ▁Tör 65407 ▁טאַ 65406 ▁kleuren 65405 ▁lidhur 65405 ▁campagne 65404 ▁atmosfera 65403 posten 65402 ▁Ferr 65402 ▁කාලයක් 65402 ▁Нийг 65400 ▁엄 65400 kohë 65399 ೬ 65398 ريات 65395 ▁කරපු 65395 ванията 65394 ▁naturen 65394 ▁veikt 65394 ▁स्तन 65394 ▁Indon 65393 ▁twar 65393 竟然 65393 Ể 65391 ▁Наши 65391 ▁qofka 65390 ▁සිත 65390 ਹਿਤ 65389 ▁GW 65389 ▁आरो 65389 ▁Klicka 65388 ▁kræ 65388 ināšana 65384 ▁gigi 65384 ▁okut 65382 ▁соработка 65382 同样 65382 ▁medical 65381 ありがとうございます 65381 ▁падры 65380 ттағы 65372 ▁slechts 65372 ▁valida 65371 ▁замеча 65371 ▁เนื่องจาก 65371 ▁cia 65370 ▁združ 65370 عرض 65367 ▁(26) 65367 ▁About 65366 સ્ક 65363 μερ 65362 Péyi 65360 ▁fazem 65360 ടിപ്പ 65359 ▁katër 65359 ▁sedaj 65359 ▁болгох 65359 ▁420 65358 aneo 65357 电脑 65357 ▁откако 65356 ▁במה 65356 ▁moraju 65355 ▁Հովհան 65355 ▁Gymru 65354 AZI 65353 ▁immagini 65353 ▁niciodată 65352 内的 65352 ▁vairs 65350 ▁njihovo 65349 ▁štý 65349 จอ 65348 ▁стоя 65348 Kur 65347 ▁mkono 65345 ▁disg 65344 ▁güne 65343 ▁Einzel 65342 ogoro 65340 ▁adqu 65340 ▁Cie 65339 짐 65339 ▁detali 65338 ▁Bland 65337 atiques 65336 inca 65336 ାନା 65336 ▁fongbè 65335 ستې 65334 ುದನ್ನು 65334 ▁обяви 65334 siq 65333 ▁свързани 65333 ároz 65331 ▁risultato 65331 ▁اسين 65329 блу 65328 ▁ζω 65328 ▁ceste 65327 ▁kinad 65326 ුවර 65325 ▁Ngal 65324 เชิง 65321 ▁ଭାଷ 65320 dele 65319 ▁saar 65319 ▁svojega 65319 гэн 65318 ווים 65317 ▁garis 65317 ▁kwake 65317 ▁কার্য 65317 ▁Yax 65316 确实 65316 ▁Tali 65315 ▁contexto 65314 しょう 65313 ertzen 65311 ▁iulie 65311 личи 65310 ▁макси 65310 ▁симптоми 65310 ▁ماز 65305 akoak 65303 ancen 65303 ▁Allerd 65303 ▁weist 65301 ሦስት 65300 ▁Mě 65300 ▁ಇದೇ 65300 ШЕ 65297 终于 65297 Ř 65296 ▁සියල්ල 65296 ինի 65295 ▁ООН 65295 ▁આવ્યું 65294 ▁köç 65293 ▁જશે 65293 ▁▀ 65290 ▁incendi 65289 ▁цвета 65288 htme 65284 ▁Abi 65284 ▁inguruko 65284 ▁жазу 65282 льника 65281 ▁sorpresa 65281 ▁gehoor 65278 ▁تغی 65278 ▁confirma 65276 ▁seneste 65276 ▁Мла 65276 ▁זונטאג 65276 ▁බී 65276 oveď 65275 ▁enny 65275 岁的 65275 OLAR 65273 ನ್ನೇ 65273 ▁RED 65273 ▁txt 65273 ilom 65272 жал 65272 ▁kunagi 65267 ▁SAS 65266 antil 65265 ▁pemer 65265 ▁ക്രി 65265 ▁дві 65264 Tak 65263 ▁Kindern 65263 ▁Poate 65263 ▁atsi 65261 ▁erfol 65261 ▁יור 65261 ▁Tous 65260 ▁Defin 65259 ▁paba 65257 ▁veld 65257 ▁યાદ 65257 ພາະ 65256 ▁wygod 65255 ▁раху 65255 isebenzi 65254 мәк 65254 ▁பாக 65254 aci 65253 ▁କରିବ 65253 ▁ከፍ 65253 ëna 65251 将会 65251 Ջ 65250 ▁arbeider 65246 ▁estratég 65245 пал 65244 ▁निकै 65244 дны 65243 ▁возраст 65243 และความ 65242 ▁авторы 65241 %), 65240 undla 65240 înd 65240 ▁permitir 65239 uzz 65238 īvo 65238 ▁Υπο 65238 vođa 65237 τομμ 65236 ▁szervez 65235 ঘট 65233 ▁conosc 65233 ▁језик 65233 ▁şark 65232 杜 65232 csoport 65231 ▁aroma 65231 nc 65230 ▁осраҡта 65229 ▁duilleag 65228 ▁საზ 65228 ሜን 65227 ▁을 65227 ▁Αλλά 65226 uluş 65225 elji 65223 ▁rechts 65223 感谢 65223 ▁njem 65222 alci 65221 ebre 65221 ▁خراسان 65221 ▁angeb 65220 ▁pore 65220 ▁положе 65220 大概 65220 xime 65219 ▁айту 65219 īšana 65218 yem 65217 ម្ប 65215 30) 65214 ▁වැඩක් 65214 ▁διαδικασία 65213 ▁konkurr 65210 ▁comunicar 65209 ▁kolači 65209 ▁ruolo 65208 itiko 65207 氛 65205 ▁محروم 65204 ▁의해 65203 ▁суча 65202 וין 65201 มุม 65199 ▁लगता 65199 ▁Šī 65196 iyon 65195 ▁určitě 65195 utak 65194 ກ່າວ 65194 ▁сумму 65194 ▁?????. 65193 ▁bult 65193 ज़ा 65192 ▁TK 65191 ▁sinon 65190 ▁Ī 65190 لماني 65189 ▁Produkter 65189 ථා 65187 ▁réserv 65187 raci 65186 ຕົນ 65186 willig 65185 ▁pequeño 65184 ▁દૂર 65182 hepha 65181 ▁postoje 65181 ▁Тэд 65179 ▁пачатку 65178 wę 65175 ▁Spir 65173 ▁μπορείτε 65172 онов 65171 ▁some 65171 ▁regime 65170 ▁વિષ 65170 ▁BW 65169 ▁نیک 65169 ▁donat 65168 ▁perangkat 65168 ▁인터 65168 ▁migra 65167 ▁müasir 65166 ukhosi 65165 ▁Unió 65163 ▁güei 65163 вяр 65161 ▁rischio 65161 ▁άλλων 65161 闻 65160 ▁ਖੇਡ 65157 ulation 65156 ▁पति 65154 हरण 65153 หวาน 65153 みな 65152 ▁ຕາ 65151 ▁разде 65150 ▁پرواز 65150 まい 65150 працоў 65148 เกาะ 65148 ▁estáu 65147 ▁праві 65147 ▁Татар 65146 ▁Lleida 65144 司法 65144 ▁světa 65143 ίκης 65142 οιραστείτε 65142 ▁valko 65142 ▁Sveta 65140 ▁diina 65140 有什么 65140 ▁Karen 65139 ▁путеше 65137 ▁bibliotek 65135 ▁भित्र 65134 ▁தெரிவி 65134 ▁ინფორმაცია 65134 റിച്ച് 65132 ▁kulttuur 65132 ▁turen 65130 rió 65129 დებარე 65129 ▁таком 65129 fordr 65128 ▁Rasa 65128 ▁menee 65128 ▁Komm 65124 ▁change 65124 ▁lemak 65124 通過 65124 Ki 65123 ไปยัง 65123 ▁នាយក 65123 えている 65123 mano 65122 ရတာ 65122 ▁1865 65121 ▁весьма 65121 ▁sheegeen 65119 ▁yada 65115 ▁сдел 65115 ▁Αθη 65113 鐵 65113 ლოა 65111 AGI 65109 шед 65109 ▁maam 65108 ▁thừa 65108 ▁äiti 65108 ത്യാ 65107 ▁şimdi 65107 ▁groussen 65106 гілікті 65105 ▁pengunjung 65105 ▁sobotu 65105 ▁काल 65105 oterapia 65104 ෙස 65102 ▁repos 65102 ▁предвид 65102 មន 65100 ▁හැර 65100 ▁Gunnar 65099 ▁rogha 65097 ग्ने 65096 ▁означава 65096 MET 65095 ▁vriende 65093 чылі 65092 十九 65092 ẠI 65091 ▁olma 65091 ▁Mpo 65090 ▁tabbatar 65090 ▁ანუ 65089 전히 65085 бот 65083 ṣọ 65083 ਪਾਲ 65082 ಂಗಳ 65079 ▁pakasaritaan 65078 ▁zaštite 65078 依法 65075 ▁málo 65074 ▁ക്ഷ 65074 品質 65074 irë 65073 തൃ 65073 აცხ 65073 مرار 65071 ▁chợ 65071 ▁szerepl 65071 ▁uste 65071 ▁weebụ 65070 ▁încep 65070 ېرو 65069 ▁Transfer 65068 areen 65067 ējuma 65065 ▁ಇಂಡ 65065 ydig 65064 ërat 65063 ▁bepaalde 65063 благо 65062 ▁saim 65062 ▁Außerdem 65060 нили 65059 ▁internazionale 65059 ▁হলো 65059 ▁രീതി 65058 ▁ගීත 65057 ▁biste 65055 emmän 65054 ▁održati 65054 ▁ກິ 65054 ममा 65052 ▁bureau 65052 ▁czę 65052 ▁Orb 65051 ▁stvor 65050 ▁Kë 65049 ▁potpuno 65049 ▁clín 65048 āma 65047 ▁ดาว 65045 ▁duuɓi 65044 ▁login 65044 ▁utakmice 65040 واعد 65039 یاء 65039 」( 65039 ▁прв 65038 յուղ 65037 ▁Pip 65037 ▁хүү 65037 ▁Ngay 65036 ▁llama 65036 éből 65034 ▁ცენტრ 65034 θι 65033 ▁دوربین 65033 ▁sempurna 65032 ியம் 65031 ▁பண்ண 65031 ▁Kusini 65027 ▁diba 65027 ▁obrov 65027 ▁strategie 65027 tiek 65025 ▁Dhi 65025 ហ៊ុន 65023 ▁pohodl 65023 ▁BAS 65022 ▁ସେମାନେ 65021 ▁პროცეს 65021 éten 65020 ▁sales 65020 ▁บน 65018 ▁üldse 65017 ბერ 65015 ▁Stadium 65015 ▁слабо 65014 ▁теро 65013 폐 65013 ▁okukhulu 65012 ▁mainit 65011 ▁moramo 65011 ▁acqua 65009 ▁annyira 65009 ▁Chad 65008 შნ 65007 拠 65007 晶 65007 ▁tidlig 65006 ▁usul 65005 ▁ລັດຖະ 65005 を使って 65003 ▁sigurno 65001 ▁کہیں 65001 ▁जाऊन 65001 ahiran 65000 ▁mangi 64998 ▁waandishi 64998 jarige 64997 ▁Açı 64995 ▁கன 64993 ▁kanjalo 64992 ▁üzərində 64992 簡単 64992 ními 64991 ▁škod 64991 ▁মধ্য 64989 概 64989 ithu 64988 ▁Jèhófà 64988 ▁igenom 64988 фра 64987 ▁fruk 64987 ▁કોંગ્રેસ 64987 ▁praksis 64985 ▁arada 64984 ▁glass 64983 ▁područja 64982 ▁וכו 64981 ▁beren 64980 ▁ਡੀ 64980 ▁참가 64980 是个 64980 ▁оквиру 64979 ▁Polen 64978 حض 64977 ▁anefa 64977 ▁בפני 64977 ▁لاش 64977 ▁Tše 64975 ▁Tõ 64974 ಾಗಿತ್ತು 64971 ▁nume 64971 ▁Usu 64970 uwun 64967 ▁namų 64967 illé 64966 ▁ומי 64966 ▁ಹೇ 64965 ▁proste 64963 Lu 64962 dá 64962 ▁jib 64962 ▁доў 64962 ORO 64961 varez 64961 ▁probable 64961 ▁تنق 64961 ▁Scar 64960 ▁zans 64960 ▁quadr 64959 ▁தற்போ 64959 ▁növek 64958 پاک 64957 ▁nazorat 64956 ▁Назарбаев 64956 16, 64955 bairt 64955 quote 64955 बद्ध 64954 waris 64952 ▁neč 64952 ▁varž 64952 ▁Δημοκρα 64952 ▁застра 64952 ▁යටතේ 64951 ▁ryw 64950 անալու 64949 ▁оскільки 64949 انیه 64948 จําหน่าย 64947 ▁uiteind 64945 ▁හැටි 64945 ▁ristor 64944 ▁ledig 64943 ▁Sla 64942 ▁konflikt 64942 ▁rendezvény 64942 ▁объектов 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62802 iyaya 62801 ▁პროდუქ 62801 غام 62800 ൊന്ന 62800 ▁predsjednika 62800 keeten 62799 ▁1911 62799 ▁fifidianana 62799 ▁Ваша 62798 ▁wszyscy 62797 ▁કરશે 62797 ždy 62796 ▁Bjar 62796 ▁lont 62795 ▁Oduu 62794 ▁Tayy 62794 ▁először 62794 ▁CNC 62793 ▁Vog 62793 ▁krie 62793 ▁כלי 62793 尖 62793 ingut 62790 opä 62790 ▁sõnul 62790 ▁Государ 62790 盾 62790 ្លៃ 62789 ▁37, 62789 ▁protokol 62789 件事 62789 āms 62788 ▁Rita 62788 keur 62786 ▁aveti 62786 ▁voie 62786 ▁viagem 62785 ▁ଦର 62784 ▁prvenstvo 62783 ሰባ 62781 ▁pardu 62781 icus 62780 ▁løs 62779 ▁ਵੇਖ 62779 ▁очеку 62777 жность 62776 ▁получите 62776 ▁اقت 62775 대한 62775 തെന്ന് 62774 ▁نثار 62773 ▁oferă 62771 ιστική 62770 ေနတာ 62770 βαση 62769 ▁Reino 62769 objectiu 62768 ▁rodiny 62768 MD 62767 ປ້ອງ 62767 ▁przeci 62767 ▁समज 62767 idł 62766 星期 62765 ▁boss 62764 ▁roli 62761 alagi 62759 ಳ್ 62759 ▁טאָן 62759 ▁ہندوستان 62759 jiet 62757 ვების 62756 ▁Clu 62756 ▁exemp 62756 кені 62755 ▁látható 62755 ▁сақта 62755 呀 62755 ▁kən 62754 ▁конца 62754 ▁Dä 62753 ▁hírek 62753 ▁plė 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62518 ▁Rossiya 62517 ▁ψυχ 62516 πτυ 62515 ▁Ísland 62515 жив 62513 ዘብ 62513 food 62512 ម្លាំង 62512 ▁suun 62511 ▁uppr 62511 ▁ويندا 62511 ርቲ 62510 ▁eurų 62510 迹 62510 ▁Ice 62508 შენ 62507 ▁terör 62506 jeka 62505 xus 62504 ▁বেশ 62504 ▁රුපියල් 62504 ッシュ 62504 ▁தேர்வு 62503 eonato 62502 ▁ffur 62501 쇼 62499 gee 62497 ▁सजा 62497 ▁ताज्या 62496 ▁ūk 62495 ▁лечения 62495 ▁פונ 62495 ▁Maja 62494 ▁Sard 62491 ▁ସୃଷ୍ଟି 62491 ጳ 62490 ▁ēr 62489 ▁spri 62488 ▁çətin 62487 olion 62486 採 62486 Austr 62485 ogyakarta 62485 ▁egyen 62485 rzy 62483 ▁இப்ப 62483 насць 62481 ولیس 62480 ▁mouri 62480 做了 62480 ▁Norden 62479 ësor 62478 和平 62476 ▁kondisyon 62475 ▁циљ 62475 igkeiten 62474 ▁relix 62473 حدى 62472 ▁голову 62472 ▁கோடி 62472 ▁sổ 62470 ▁visiškai 62470 kowych 62469 andak 62468 ▁атап 62468 ▁fòrma 62467 ▁hamma 62467 ▁bilisummaa 62465 ▁சர் 62465 ONS 62464 ▁ape 62462 zések 62461 ▁opdater 62461 ▁piis 62461 ▁gücl 62460 ▁гай 62460 вене 62458 ▁Från 62457 ▁реви 62457 ▁જેમાં 62457 ▁അറിയ 62457 ljivosti 62456 ▁существо 62456 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کرا 61150 ▁ants 61150 说的 61150 ായം 61149 isaan 61148 ▁algorit 61148 ▁rakent 61147 ▁Григо 61147 ▁banan 61146 ▁potro 61146 ▁ээ 61146 ▁bëjë 61145 ▁ಹಿರಿಯ 61143 BAN 61142 ▁segura 61138 ńskiej 61136 נגט 61136 ▁виклика 61136 ▁هوء 61135 ▁কমিটি 61134 ▁Federación 61132 ▁والب 61131 ▁പ്രതീക്ഷ 61131 уыҡ 61130 ▁debemos 61130 ▁Viele 61129 ▁instan 61129 ▁urspr 61129 ▁jednocześnie 61128 ▁kampa 61128 ▁යුතුයි 61126 ▁relativa 61124 կին 61122 ▁hảo 61122 óbbi 61121 ▁Hogy 61121 进步 61121 ▁application 61120 ▁สมัครสมาชิก 61120 खु 61119 ▁shin 61119 லுக்கு 61117 ಿಕಿ 61117 ▁Records 61117 ▁desarrollar 61117 KER 61116 isseur 61116 isuudessa 61116 ▁Aisce 61116 ▁stu 61115 ớt 61114 ▁ontdek 61114 ▁крем 61114 ▁course 61113 ▁тонн 61113 取引 61113 ▁løbet 61108 ▁трэ 61108 [8 61107 ▁обър 61107 ▁acomp 61106 越し 61106 wikkel 61105 ▁vole 61105 ▁Objek 61104 ▁ræ 61104 ▁غزة 61104 ▁orda 61103 ствием 61102 ▁Alz 61101 ▁ګوند 61101 ceria 61099 ▁faud 61099 ▁hoá 61099 ▁פנים 61099 ▁სამხედრო 61099 ▁βάρος 61098 ▁отлича 61098 ▁podoba 61097 ▁setter 61097 ▁въпро 61096 本来 61096 گرام 61095 ▁urb 61095 ▁быйыл 61093 tol 61092 ▁djevoj 61092 ▁izskat 61092 ▁problèmes 61092 ▁нагру 61092 ▁спец 61091 ণা 61089 ▁нийс 61089 সঙ্গ 61088 ▁takiej 61088 ▁কোটি 61088 求め 61087 НИЕ 61086 গত 61086 ▁있지만 61086 ▁Oliv 61084 评价 61083 barth 61082 ▁soñ 61082 ▁crecimiento 61081 ▁тука 61081 पोर्ट 61080 ौती 61080 月に 61080 ▁Brasília 61078 ▁keskustel 61078 joni 61077 Ņ 61075 ▁markedet 61075 ▁જગ 61075 ▁53. 61074 okrát 61073 pois 61073 ▁ಕಡೆ 61073 ▁denom 61071 ▁ڪهاڻي 61071 ▁заинтере 61070 ダウン 61070 ▁सल्ला 61069 дае 61068 ວຽນ 61068 ▁bhli 61067 ▁sicuramente 61067 скери 61066 ▁arritur 61066 ▁communic 61064 ▁urmare 61064 ▁আহত 61064 hemmin 61063 ▁استقبال 61063 ▁ବ୍ଲ 61062 ▁souff 61061 ▁රික 61061 ▁casual 61060 ▁гадзі 61060 ▁umma 61059 ▁güçlü 61057 ▁klap 61057 ▁газета 61056 ▁კრი 61055 の場合は 61055 ▁przypom 61054 ▁ziel 61054 ▁процесса 61052 ▁тат 61052 ▁Gaill 61051 اگر 61047 旦 61047 ▁aşağıdakı 61043 ▁แนว 61043 ▁mesecev 61042 ▁згідно 61040 ▁magar 61039 ▁dettagli 61036 ▁transak 61035 ▁हमारी 61035 ▁natal 61033 ▁repro 61033 »։ 61032 နှစ် 61032 ▁Почему 61032 ▁kompakt 61031 ▁ممنون 61031 ▁جدی 61028 と思い 61028 רון 61027 ંભ 61027 ▁вице 61027 ▁аты 61026 ▁nuesa 61025 ▁þró 61025 ▁απε 61025 θώ 61024 ▁poprav 61024 ▁зашто 61024 ▁המד 61024 ▁ಬಳಿಕ 61023 ▁саба 61022 ▁HÀ 61021 臭 61021 ▁Všet 61020 ▁samedi 61019 zije 61018 ▁Rusijos 61018 აშვილის 61016 ▁hyggelig 61016 ▁trudno 61016 ▁Teresa 61015 ▁wyra 61014 ▁آمريڪا 61014 ▁ବିକାଶ 61014 ในช่วง 61012 ▁କରୁଛନ୍ତି 61012 ▁незалеж 61011 ▁бойы 61010 iskie 61008 லகம் 61007 ▁rovnak 61007 ฝ้า 61006 ulmány 61005 рыҫ 61005 ▁podporu 61004 ▁1998, 61003 förande 61001 ▁90. 61001 ▁miksi 61001 ▁amani 60999 ▁Liep 60998 ▁dilyn 60998 ▁затво 60997 ▁léka 60996 ▁necesare 60996 ▁şəhəri 60995 ▁улице 60994 ediu 60989 ▁Zü 60989 ▁jout 60988 体制 60988 ▁directement 60987 ▁الرق 60986 ないので 60986 نك 60984 ▁phóng 60984 nené 60983 տեմ 60983 ▁írás 60982 ▁چاہیے 60981 meter 60979 عال 60977 ▁sincer 60976 ▁انگیز 60976 ▁উঠ 60975 ▁ehtiya 60974 ibuze 60973 ▁adevărat 60973 ▁cumpar 60970 ▁nikak 60970 ▁буди 60969 อยู 60968 elum 60967 स्तर 60967 ▁دائر 60967 ▁проводить 60966 णारी 60965 ▁Topp 60965 akanya 60963 ▁ayan 60963 ոյեմ 60960 ▁calle 60959 ▁කළේය 60959 ▁کامیاب 60957 ▁тепер 60956 ▁پاور 60955 タル 60953 inās 60952 مكن 60952 ▁descubrir 60951 ▁kaub 60951 իտի 60950 ▁masuala 60950 ▁هميشه 60950 формира 60949 ▁Nice 60949 ▁imposs 60949 ▁وصف 60949 ષ્ય 60948 ▁Rit 60948 ▁komin 60947 ▁மிகவும் 60947 gunakan 60944 ができる 60944 直到 60944 ▁KAB 60942 ▁sluš 60941 ▁فرز 60941 ▁mante 60939 ▁ශා 60939 ▁celles 60938 ▁ಪಡೆದ 60938 ▁деј 60937 ▁лан 60937 ▁Gap 60936 ▁адамның 60936 ▁ඇර 60936 ▁Косова 60935 ▁получен 60935 ▁معاش 60934 ▁stiùireadh 60933 ▁ਦੱਸਿਆ 60933 れます 60933 ovač 60932 ▁Vezi 60931 ETO 60929 ▁IBM 60928 ▁Pasta 60928 ▁Warszawie 60928 ▁الجن 60928 שימת 60927 ريو 60927 ▁старонкі 60927 ▁Kutil 60926 ႆ 60924 categ 60923 पद 60923 hmä 60922 שיים 60921 ▁Kuta 60921 sea 60920 ítható 60920 구요 60920 bean 60918 ▁ссыл 60918 મ્પ 60915 ▁traum 60915 وانی 60914 ையான 60914 ▁riamh 60914 .10 60913 を読む 60913 ▁মতো 60912 aib 60911 ▁csatlako 60911 coni 60910 ▁Līdz 60909 ▁প্রস্ত 60909 ▁тэрбум 60908 ▁būd 60907 熟悉 60906 AMO 60905 ▁Hasta 60905 ຫລາຍ 60904 ▁بگی 60903 ▁DIG 60901 ▁امسال 60901 法人 60901 ▁stuð 60900 ▁hrd 60899 ရွား 60898 ▁Chinese 60898 ▁maaliskuuta 60897 lığında 60895 ▁poboación 60895 ▁بهذا 60894 indahan 60893 хвати 60893 ▁เมือง 60893 rium 60892 zing 60892 ცო 60891 ▁tapo 60891 ájom 60889 ▁tồn 60888 ведамі 60887 ▁gaid 60887 ▁כולם 60887 ազդ 60886 ▁completar 60885 ▁ensimmäinen 60884 ▁▄ 60884 észen 60882 ▁anunt 60881 ▁raga 60881 ▁נאָ 60881 ступі 60880 ▁berfungsi 60880 ▁harreman 60880 ▁imzal 60880 ▁ಕಲೆ 60880 16) 60879 fiin 60878 ▁parroquia 60877 மூ 60876 ▁Швейца 60875 муля 60874 ▁ustav 60873 ▁багать 60873 ofar 60871 الى 60871 Sel 60870 زاز 60870 ▁رمز 60869 fassung 60866 iniams 60866 ▁jierren 60864 ختلف 60863 ▁rokmi 60863 ▁concreto 60862 ▁dereito 60862 ▁מאז 60862 तीने 60861 ▁ពាក់ 60861 ▁mboka 60860 ▁Stutt 60859 ▁algem 60859 ▁Toda 60858 ▁நக 60858 ▁Yani 60857 ▁sirna 60857 ▁Лука 60857 ▁توصیه 60857 ▁ജയ 60856 iblí 60855 ▁Güven 60855 ▁મદદ 60855 限り 60855 ▁yimid 60854 ▁преу 60854 ▁ਦਿਲ 60854 ▁вопросов 60853 ባዊ 60850 ▁crescita 60850 ▁biç 60849 åp 60848 ยาง 60848 បើក 60848 ▁Azad 60848 Amerika 60847 ແນວ 60846 ▁nakt 60846 ▁نقشه 60846 ▁skrifa 60845 ပို 60844 asjes 60843 ▁рат 60843 ufer 60841 ровали 60841 ▁centim 60841 ▁اكثر 60841 iertos 60840 ▁einu 60839 φορές 60837 ▁йом 60837 ▁rolle 60836 enea 60835 uré 60834 ▁pravila 60834 ▁fheàrr 60833 enix 60832 уны 60832 කයන් 60831 ▁zľ 60831 ▁нутаг 60830 ▁یونیورسٹی 60829 ▁kwietnia 60827 ▁pocu 60827 ▁தனது 60826 14) 60825 ▁bér 60825 ▁بنگ 60825 wari 60824 ўро 60824 ▁Respublika 60824 ▁предлагает 60824 लें 60823 ▁Jezus 60822 ifanya 60821 арал 60820 ▁joriy 60820 ▁méret 60820 ▁недела 60820 ▁Dünyanın 60819 úgy 60817 ▁nedostat 60817 ▁ered 60816 ▁resultatet 60816 ▁מוק 60813 ▁वीर 60813 雰囲 60811 Day 60810 മ് 60810 Link 60809 technik 60808 ▁Manuf 60807 ▁geholl 60807 ▁سقوط 60805 ▁아마 60805 ▁σημαίνει 60804 ▁выпуска 60804 ▁zlepš 60803 basan 60801 лоҡ 60800 ▁mkp 60800 ▁Büyükşehir 60799 ▁Komple 60798 ▁२६ 60797 ælland 60796 ▁dụ 60796 ▁geski 60795 ліна 60794 ▁дзяў 60794 ▁лабора 60794 ▁طري 60793 日子 60793 ▁angol 60792 ▁sanhi 60792 ▁pakk 60791 ▁vnú 60788 ▁மருத்துவ 60788 ▁Въз 60787 ▁domina 60786 ▁utbildning 60785 ▁відно 60785 ierungs 60784 ZP 60781 ▁nebezpe 60781 ▁Југослави 60781 ▁ағзаһы 60781 ▁فقال 60781 ▁පද 60781 ▁nõu 60780 ▁Међу 60780 ▁महीने 60780 ▁secteur 60778 ▁bekende 60777 ▁nny 60776 ▁Libert 60775 ▁ស្ថិត 60775 ▁ګډ 60774 ▁udfordr 60773 ▁නිර්මාණය 60772 מיר 60771 ▁كامل 60769 갑 60769 ▁رف 60768 ▁አለው 60768 ▁жив 60767 ▁ສັງ 60767 诊 60767 ngaasi 60766 ূল 60765 ▁gewesen 60764 ▁អង្គការ 60764 важ 60763 üğ 60761 पानी 60761 ▁Whatsapp 60761 ▁parlare 60761 intia 60759 ▁Conven 60759 ▁skö 60759 ▁упла 60759 ▁środków 60758 植物 60757 ▁techniques 60756 shimcha 60755 ▁desto 60755 전자 60755 ynet 60751 منت 60751 önen 60750 ▁kvöld 60750 ▁vroeg 60750 wór 60749 ▁Vro 60749 ҡай 60748 ดิต 60748 ▁Nairobi 60748 ▁אשכול 60748 ▁Aid 60747 inket 60746 वळ 60746 移民 60746 ▁ekte 60745 ▁inkişafı 60745 ▁Lisboa 60744 ▁ää 60744 agent 60743 ▁mẽ 60743 ▁байқа 60743 ▁അച്ഛ 60743 ▁ගන්නා 60741 yib 60740 ▁капаците 60740 ▁করছে 60739 ungtungan 60738 ▁tvrd 60738 ▁végig 60738 ▁बिज 60738 оби 60736 ▁wund 60736 content 60735 ▁čov 60735 ▁obecnie 60734 ▁βρε 60734 ▁aven 60733 ▁পি 60733 faz 60732 ynlig 60732 ▁Decreto 60732 ▁française 60731 vállal 60730 ▁Clim 60729 ▁aksep 60729 ▁තිබුණ 60729 ▁käy 60728 ▁mers 60728 ▁йән 60726 प्स 60725 ▁hant 60724 ക്കുറ 60723 ▁вероят 60722 Gem 60721 ▁управле 60719 ▁ಬರುವ 60719 andishi 60718 ▁Drž 60718 ▁інформація 60718 ▁Առաջ 60717 ▁മറ്റൊരു 60717 ▁Bekijk 60716 វាង 60715 ▁jorden 60715 ▁oromoo 60715 েন্স 60714 ຫນືອ 60714 ▁sierp 60714 ▁समर्थन 60714 ▁ລັດຖະບານ 60714 ▁Téc 60713 ▁Kecamatan 60711 ▁gestió 60710 တစ္ခု 60707 ▁obodo 60707 ▁ზღ 60704 ▁funguje 60702 ▁uztur 60700 жает 60699 ▁Sten 60699 ▁próbál 60699 ▁вәки 60699 Ci 60698 ▁Feld 60698 ▁mozg 60698 ▁fucked 60697 ▁الشعر 60696 ສະຫ 60695 ▁físico 60695 ▁paris 60695 कांना 60694 ▁Detail 60693 ▁međ 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▁Reading 59816 ▁Yemen 59816 ▁Qey 59813 न्ति 59812 ▁Bow 59811 ▁blízk 59811 ▁ɗun 59811 ▁ქართულად 59811 経営 59811 čnog 59810 ▁Battle 59810 ▁Também 59810 ▁Moje 59809 ▁wakt 59809 ▁Jie 59808 ▁dodatno 59808 ▁کډ 59807 ▁১৬ 59806 ▁Liidu 59805 ▁inder 59805 ismul 59804 தோ 59804 tsch 59803 ▁moeder 59803 thar 59802 ებო 59802 ▁Мя 59802 ▁әрекет 59802 ▁уурхайн 59801 ▁Ọrụ 59800 ▁when 59799 षिक 59798 akaz 59797 ìos 59797 лдән 59796 ▁ផលិត 59796 ▁secon 59795 szy 59794 ▁Industrie 59794 ប្រុស 59793 hethiwe 59792 ▁කරුණු 59792 ▁kísér 59791 ▁ເຂົາ 59790 etri 59789 ▁ተጨማሪ 59788 ayatan 59787 ாரா 59787 пала 59786 ▁Ricardo 59786 ▁забезпечу 59786 ランス 59786 은행 59785 ▁zoeken 59784 ▁گوشت 59784 ▁එරෙහිව 59784 ትም 59782 ውሞ 59780 ▁Omdat 59780 ▁tk 59778 mények 59777 ▁Gramm 59776 ▁السنة 59776 ▁akşam 59775 ▁мөх 59775 ▁محتوا 59775 தாவது 59772 ۱۳ 59771 ዋሚ 59771 สั่งซื้อ 59770 ▁tidspunkt 59769 atiouns 59767 ▁Mm 59767 ▁Ress 59767 ▁אותנו 59767 ▁ଘରେ 59766 ▁Account 59765 ▁Luar 59765 баво 59764 غت 59764 ▁Dul 59763 ▁interval 59763 ▁نز 59763 ▁MX 59762 ▁dagus 59762 avait 59761 ▁naqu 59761 ▁килде 59761 ▁ਗੁਰਦੁ 59761 ▁dijete 59760 ▁учраас 59760 原本 59759 tieve 59758 ▁Drug 59758 ▁[5] 59758 ഗം 59757 ▁целия 59757 ▁etkinlik 59755 ▁противопо 59755 ▁ដំណើរ 59755 ▁Parker 59754 ▁joulukuuta 59754 ▁kompat 59754 ▁егет 59754 ▁Шар 59753 ▁ustrez 59750 ▁سرور 59749 arnir 59748 -2011 59747 ▁Catalana 59747 ▁Erdogan 59747 gets 59746 ▁університе 59746 面白 59746 ิช 59745 ▁toilet 59744 adai 59743 acin 59742 ▁erməni 59742 ▁margar 59742 ▁астро 59742 ▁lignende 59741 ▁قوات 59741 ▁ομο 59739 änken 59738 ▁ಪೆ 59738 ▁цагийн 59736 ▁بادش 59736 提醒 59736 ▁wenz 59735 ▁excl 59734 ▁зохицу 59732 estimm 59731 ຕ່າງປະເທດ 59731 ▁شرم 59731 पू 59729 ▁ausgest 59729 ▁vähem 59729 ringen 59728 luta 59727 мок 59725 лери 59724 ▁έρευνα 59724 作出 59723 ▁Toy 59722 詞 59722 හස් 59720 ▁3.1 59720 ▁diagnosti 59720 ▁карыста 59720 ▁يحت 59720 ▁തെളി 59720 ាករ 59719 ▁Kiswahili 59719 ▁uș 59719 ▁Чор 59719 ▁zondag 59718 üüd 59717 περ 59717 ▁xD 59717 ▁okreś 59715 ▁phe 59715 ▁souhaite 59714 ▁공연 59714 ▁okushiwo 59713 ▁குறித்து 59713 登場 59713 ▁فرما 59712 ▁обществе 59711 리즈 59711 ▁ами 59709 ▁очите 59707 ▁Дер 59706 ▁اوباما 59705 ებიდან 59704 ኽ 59704 gyn 59703 ▁simp 59703 ▁ವಿವಿಧ 59702 /2019 59701 astos 59701 qayıt 59701 ▁универзите 59701 umna 59700 egle 59699 ▁الشركات 59699 ▁tsaro 59698 ▁ซี 59696 loga 59695 road 59695 ▁toka 59695 позна 59693 usko 59692 حان 59692 ▁dár 59692 ▁svým 59692 rouwen 59691 ավիր 59691 ▁amatør 59689 ▁шаардлагатай 59688 切り 59688 arsu 59687 বী 59687 ▁الوز 59687 ▁دخت 59686 ▁файл 59685 ▁цивилиза 59685 ል፤ 59683 otdeauna 59682 ▁scegliere 59682 ▁очеред 59681 ▁хотели 59681 ▁ഭരണ 59681 ▁Permainan 59680 ezion 59679 ▁Ular 59679 ▁fwa 59678 ▁Avr 59677 ▁annd 59677 ▁dienā 59676 stol 59675 ▁päivänä 59674 ▁ижад 59673 ▁अश् 59673 バス 59673 edaes 59672 ▁कौ 59671 িভ 59669 ▁Record 59669 עו 59668 ▁patrí 59668 ▁мои 59668 ▁повто 59668 cente 59667 эф 59667 ▁Trek 59667 ▁яҡлау 59667 plads 59666 แก้ 59666 ▁Varför 59666 nais 59665 дерді 59665 ▁emergen 59665 ▁Doug 59664 ▁tilh 59664 維持 59664 带着 59661 ▁Ў 59660 wung 59659 ▁Fakat 59659 ▁Rhein 59659 ▁Oczywiście 59658 ▁ubing 59658 ▁Napi 59657 ▁reprodu 59657 ▁ölü 59657 ▁сәбәпле 59657 ▁üm 59656 稿 59656 ▁Siapa 59655 ▁abonnement 59655 USI 59654 ▁Сибай 59654 ্বর 59651 kute 59650 ▁остается 59650 ခ်မ္း 59649 ▁noir 59649 ▁اداروں 59649 ▁tys 59648 галі 59647 เกอร์ 59647 ▁lyn 59647 берг 59646 ುದ್ಧ 59646 ▁levert 59646 ▁títol 59645 СТА 59643 هيو 59643 තම 59642 ▁Pál 59642 ▁anmeld 59641 ▁ਵਿਦਿਆਰਥ 59641 観光 59641 ▁ilustra 59640 ▁základní 59639 ▁copiilor 59637 くて 59637 ▁maslah 59636 ங்கிய 59635 belt 59633 ▁Mysl 59632 ▁ganacs 59632 ▁कम्युनि 59632 ▁जस्तो 59632 ▁wangu 59631 ▁дөрөҫ 59631 ▁conden 59630 ▁gcuid 59630 ▁lapangan 59630 ▁खिला 59629 ▁офис 59627 τιω 59626 astada 59625 ▁dív 59625 adach 59624 נאי 59623 ▁derbyn 59623 ▁damu 59622 ▁राशिफल 59622 ▁pouk 59619 μως 59618 ▁δύνα 59618 ▁ತೀ 59616 activitat 59615 șit 59615 ▁opção 59615 ▁smukke 59615 alselt 59614 ▁Президента 59614 јен 59612 ▁normalmente 59611 ▁удиви 59611 ærk 59610 θήκες 59610 ubuk 59609 พลาด 59609 ▁siit 59608 firði 59606 արին 59606 ظيف 59606 ▁gjyk 59606 Off 59605 ▁дәрі 59605 ▁जिम्मे 59605 ▁компют 59603 ▁учасників 59603 ▁Nac 59602 ▁umožňuje 59602 ▁Нам 59602 ▁patienter 59601 ösa 59600 ▁meyd 59600 ▁आधी 59600 ▁(?) 59599 ത്തിനും 59598 ▁imkoniyat 59598 ekw 59597 ▁aquele 59597 เกมส์ 59596 ▁duración 59596 ▁partij 59596 ▁примеру 59595 ästi 59593 ▁9.00 59593 ▁готовы 59593 alariga 59590 ▁XD 59590 ▁hokej 59589 ▁знову 59589 ▁позиция 59588 μηση 59586 ▁perfecta 59586 ▁xarunta 59586 ▁razmer 59584 ētāju 59583 γνώ 59583 ▁туҡ 59583 ▁घट 59583 éa 59582 ▁Kyllä 59582 ▁balance 59582 ▁چپ 59582 ▁cser 59580 ▁כמובן 59580 ▁राशि 59580 ома 59578 ומית 59578 ▁Christmas 59578 ▁indeks 59578 ▁αγά 59578 娜 59578 ▁Mitglied 59577 денти 59576 ▁shko 59576 ▁səs 59576 ylab 59574 óbb 59574 ▁znanst 59574 ▁اوږ 59574 ▁דזש 59572 ▁Maison 59571 ▁erne 59571 ▁käytänn 59570 ▁prehr 59569 ▁хотела 59569 ମିତି 59568 ▁Kuj 59566 ▁ნამდ 59566 ▁CAD 59565 čnik 59564 ifat 59563 ▁Racing 59563 ▁זכ 59563 ▁कानून 59562 ුවන් 59561 ▁इथे 59561 ਜੂ 59560 коно 59557 чара 59556 ▁Uray 59556 ▁aice 59556 ▁korak 59555 ▁مسلح 59555 ▁कार्यकर्ता 59554 ▁uger 59552 ▁କୃଷ 59552 ▁Zero 59551 ときは 59551 द्वारा 59550 ▁مساء 59549 angl 59548 ководи 59548 ▁PornHub 59548 ▁muller 59548 jában 59547 ▁зы 59547 ▁akzeptéiert 59546 ▁عذاب 59545 ▁பக்கம் 59545 ▁recuperación 59543 ▁ნუ 59543 enet 59542 ▁Čas 59542 ▁setja 59541 ▁sisään 59541 ंभ 59540 artas 59539 ▁degr 59539 ▁waana 59538 ▁Иҫ 59538 ▁элемент 59538 ▁رضی 59538 ▁kijk 59536 ▁kumb 59536 ▁kuriem 59535 ▁lieč 59535 ▁վիր 59535 ▁ସାଧାରଣ 59535 ▁toid 59534 arty 59533 ▁ansvarlig 59533 ▁circunstan 59533 ▁immobili 59533 ▁ڀٽو 59533 ▁Defensa 59532 ▁conocida 59532 ▁memutuskan 59532 ስራ 59531 ▁သူ႔ 59531 ▁所以 59529 ▁educativa 59528 ▁μνη 59528 ίζονται 59526 ▁udzia 59526 ▁torre 59525 セット 59523 ▁zúčast 59522 ▁આરો 59521 肯 59520 ▁joista 59519 DK 59518 يمق 59517 ▁АР 59517 ▁сауҙа 59517 ▁kontrakt 59516 ▁ರಾಜಕೀಯ 59516 iesc 59515 吐 59515 स्वी 59513 ▁atendimento 59513 ▁mòran 59513 ▁сығанаҡтан 59513 ▁255 59512 ▁konsul 59512 로부터 59512 ▁заштите 59510 hoari 59509 ▁Fru 59508 ▁Ikast 59508 ▁znacznie 59508 koak 59507 ▁maí 59507 ▁কার্যাল 59506 ▁işləri 59505 ▁Κου 59503 ॉप 59502 ▁Voki 59502 ▁साथी 59502 पटक 59501 ▁bys 59501 ikken 59500 ប្រកួត 59499 ▁WI 59499 ▁поде 59498 figur 59497 ▁Борисов 59496 ▁покрај 59496 ▁بنفس 59496 βι 59494 母亲 59493 чування 59492 ▁contor 59492 ▁meen 59490 ▁justru 59489 ▁десяти 59489 ▁هاڻ 59489 ▁Irland 59488 ▁Witam 59486 क्रमण 59485 ▁possu 59485 ▁مطم 59483 baahinta 59482 ▁kisz 59481 ▁médicos 59481 ▁skemmtil 59481 ▁ваҡытында 59481 ▁أمس 59481 ლებას 59480 ▁zej 59479 ulev 59478 ▁mji 59478 ▁njëjt 59478 ▁Šp 59478 ▁даў 59478 ▁քաղաքապետ 59478 ▁اتفا 59478 ▁tàn 59476 ▁아침 59476 ▁izvor 59475 ▁partneri 59475 ấc 59474 ▁वर्त 59474 ▁Сондықтан 59470 ▁Medlem 59469 ▁jirto 59469 ળતા 59468 ▁Эти 59468 ▁સંબંધ 59468 ▁неке 59466 ▁ವೇಳೆ 59466 ▁vết 59465 ústria 59464 ленген 59464 ▁ifrån 59463 ▁nhấn 59463 ▁zagotov 59463 ▁Kamis 59462 ▁لاهور 59462 ranie 59461 ▁внатре 59460 ▁Барлық 59459 īva 59458 ▁مالک 59457 ▁kräv 59456 umsal 59455 ▁ארום 59455 esnis 59453 νους 59453 ▁Fisc 59451 來說 59450 ▁liebe 59449 ▁ಕಡಿಮೆ 59448 ▁návrh 59447 ▁محتو 59447 ▁ആത്മ 59447 osting 59446 วัสด 59446 لل 59444 ▁Aceh 59444 विष 59443 ▁bicicleta 59443 ▁каким 59442 ▁ရာ 59442 ▁princí 59441 grupper 59440 колико 59440 imd 59439 ▁NOS 59439 ▁ønsk 59438 ▁продукції 59438 ሄር 59436 ▁задржани 59436 ▁अधिका 59436 diyini 59434 ન્દ 59432 ▁kuriuos 59432 комиться 59430 ▁пъту 59430 qqa 59429 зета 59429 ▁وتن 59428 zich 59427 adság 59426 ▁مدیران 59426 ▁ನಿಜ 59426 ▁bygget 59424 チーム 59423 ڑھ 59422 中共 59422 允 59422 ▁shtr 59421 ▁Κοι 59421 ▁Constru 59420 ▁Kontroll 59420 ▁kontrib 59420 ▁साउन 59420 ▁pangalan 59419 ▁אוק 59419 กรุงเทพ 59418 ninga 59417 ▁elemente 59417 ▁úrad 59417 ▁малку 59414 ▁парламента 59414 +, 59411 ЛТ 59411 եշտ 59411 ▁ಭಾರತೀಯ 59411 oara 59410 ▁Formula 59410 ▁эпо 59410 țiilor 59406 ▁memnun 59406 튜 59405 atering 59403 ▁scre 59403 ▁उड 59403 ▁мэргэжлийн 59401 ▁cadena 59400 ▁ekri 59400 ▁ziara 59400 下記 59400 δεικ 59396 ▁земја 59396 ▁ഞങ്ങള് 59396 ນະຄອນ 59395 ▁Telev 59394 ▁Zob 59394 ▁Paulus 59393 ▁construção 59393 ээгүй 59392 ▁Crit 59392 ▁WE 59392 ▁Waf 59392 ▁konkuren 59392 ஐ 59391 ▁ወንድ 59391 ▁segona 59390 ▁متوسط 59388 ▁쿠 59386 ettare 59385 owiedzi 59385 ▁فيلم 59385 मय 59384 ▁imod 59384 ▁játsz 59384 ▁заўсёды 59384 ▁ediblər 59383 чнае 59382 ▁উপর 59382 चार्य 59380 ▁cümlədən 59379 hoar 59378 ▁jesteśmy 59378 ▁necessari 59375 ይን 59374 ▁laikas 59374 ▁साँ 59374 ▁ಮಂಡ 59374 ▁köy 59373 ▁acestuia 59372 ▁भर् 59372 ▁ေရး 59372 是有 59372 ▁kosong 59371 メント 59371 ▁обек 59370 つまり 59369 ▁Gwel 59367 ▁нарушения 59367 ▁ବଡ଼ 59367 ▁stell 59366 ▁udaler 59366 pués 59363 ▁karong 59362 ▁synty 59362 ▁لمع 59362 azos 59361 ▁ປະກ 59361 edyada 59359 שיא 59359 ▁енциклопе 59359 فيذ 59358 ჭირდება 59358 ▁gerir 59358 葡萄 59358 ▁գրանց 59357 ▁बस् 59356 ▁ликви 59355 ▁оған 59354 ▁नीचे 59353 ▁መደ 59353 ▁ልብ 59352 ▁cani 59351 iencia 59350 ▁bahe 59350 ▁етештереү 59350 ▁최고의 59350 بور 59349 ▁eredeti 59349 ▁서비스를 59349 ENTO 59348 лами 59348 ▁Cuir 59348 ▁kontakta 59348 ▁بداية 59348 ikiliza 59347 VAT 59346 osofi 59345 ▁nate 59345 ▁bry 59344 ▁snaží 59344 ▁tanács 59344 ▁ನ್ಯಾಯ 59344 деб 59342 enaan 59341 ▁бүлеге 59341 绝对 59341 ukuru 59340 ▁դրանց 59340 atiche 59338 нева 59338 規模 59338 বাজার 59337 ▁VW 59337 ▁پاسي 59337 ▁confianza 59336 ិល 59335 ▁jelema 59330 ▁हाला 59330 ▁виси 59329 лева 59328 ▁vides 59328 ▁ਸੁਣ 59328 аас 59327 例えば 59327 keszt 59326 ëmmen 59325 ▁Baada 59325 িয়ায় 59323 ▁doğal 59323 ▁사항 59323 hauri 59322 ▁Кто 59321 เติม 59319 ▁pye 59319 ▁suasana 59319 ▁банки 59319 ▁निकाय 59319 ▁დემ 59318 vz 59315 والی 59315 ▁дистан 59315 ▁чалаве 59314 ▁nà 59313 ድሮ 59312 ▁prepa 59312 갤 59312 ยังมี 59310 əklə 59309 ▁Londra 59309 ▁infrac 59308 ▁ёй 59308 ▁૫ 59308 らし 59308 лому 59307 ▁razloga 59307 ▁ഏറെ 59307 ギー 59306 asak 59305 rå 59305 ادهای 59305 ▁keselamatan 59305 ▁૪ 59305 כך 59302 ▁רצי 59302 ▁の 59301 楽しい 59301 fundo 59299 ▁aranjeunna 59298 ▁նշել 59297 ர்ம 59296 ▁Ministarstvo 59296 ▁صدای 59295 ửi 59294 ▁əz 59294 有限 59294 kp 59292 ľúben 59292 ▁fira 59292 ▁abashayeli 59291 ▁titi 59291 ▁হল 59291 使得 59291 zeum 59290 ▁жители 59290 ▁ويون 59290 ▁refuz 59288 ▁sitte 59288 Ori 59287 ẾT 59287 ▁femen 59287 ▁kõiki 59286 ▁svin 59286 ▁яҙа 59286 ▁razvoju 59285 ▁معی 59285 ▁auditor 59284 ▁följa 59284 ▁aparte 59282 ▁paig 59282 ▁Špan 59282 lalo 59281 ▁نيو 59280 17) 59279 ▁машины 59279 世界上 59279 wap 59278 ▁Erasmus 59278 දනය 59276 ▁líon 59274 ▁primele 59273 ▁дэд 59272 ິວ 59271 그래 59271 şaq 59270 ▁розраху 59270 enzo 59269 ▁مبتلا 59269 休息 59269 有利 59269 ekoa 59268 epa 59268 ការងារ 59268 ▁construction 59268 ▁საქმ 59268 贸 59268 ▁Qər 59267 ▁ព្រោះ 59266 ▁imagens 59265 ▁stratég 59264 ▁коали 59264 ▁kjenner 59263 ▁suorit 59263 ▁мес 59263 ighdeán 59262 ologique 59262 ▁toukokuuta 59262 ▁مقامات 59261 abhas 59260 bladet 59260 buatan 59260 ▁ژوب 59260 ánach 59259 ▁тэм 59259 дарына 59258 ▁ВІ 59258 ▁גדולה 59258 struktur 59257 ▁discapac 59257 ରିଆ 59256 ▁FX 59256 ▁razisk 59256 ▁uop 59256 uyi 59255 ណ៌ 59254 ▁actualmente 59253 ▁manan 59253 ▁vyhr 59253 ▁دریا 59253 bende 59252 ▁symud 59251 .[3][4] 59250 ▁įvy 59249 ▁სპორ 59249 ▁უფას 59249 ▁Leita 59247 стэрства 59246 ▁huzur 59245 цыйны 59244 וסר 59243 ▁màger 59243 ▁pleno 59243 ▁мову 59243 Sec 59242 naz 59242 ਰਿੰਦਰ 59242 fhéad 59241 isista 59240 ▁Hrvatski 59240 ▁Muy 59240 ▁Београда 59238 ▁Wana 59237 ▁transparent 59236 weuh 59235 鸡 59235 ▁بزر 59234 ▁୧୧ 59234 ▁пожар 59232 80. 59231 ▁medieval 59231 ▁فاصله 59231 ਤਲ 59230 ញ្ជ 59229 ▁Malay 59229 ▁eksklus 59229 ▁Reklam 59227 ▁Изра 59227 ▁الرجل 59227 ▁Kész 59226 ▁αρχή 59225 ▁Rural 59224 ▁अगदी 59224 ▁mbụ 59222 ▁posil 59222 ▁барған 59222 ダイエット 59222 ▁establecer 59221 ▁hyvän 59221 ▁mett 59220 ▁paremmin 59220 尝 59220 ▁Kyl 59219 ▁baybay 59217 ▁profiter 59217 禁止 59217 ▁kaayaan 59216 ▁ค้น 59215 ▁dejtingsajter 59214 ▁Өмн 59214 ومن 59213 elok 59212 ▁tenn 59212 ▁σημείο 59212 ▁матэрыялаў 59212 hw 59211 ▁programación 59211 ▁هوشمند 59211 ▁한편 59211 ▁SEG 59210 ▁επισκέ 59209 ▁obje 59206 ▁opportun 59206 ▁банку 59205 ▁okrem 59203 ▁влиза 59203 ▁страх 59202 riä 59201 ਗਮ 59201 ▁könnt 59201 ▁вирту 59200 ▁प्रधान 59199 itionen 59197 ▁सिनेमा 59197 ខ្លាំង 59196 ▁نتيجة 59196 ▁ngir 59194 ▁большинство 59194 ▁водите 59194 ▁כסף 59194 ▁കൂടുതൽ 59194 애인 59194 ▁evid 59193 ▁животот 59193 bliques 59192 ▁fakultet 59191 ▁иденти 59191 !' 59189 ôs 59189 шына 59189 ▁duyg 59189 ացող 59188 ▁coneg 59188 ▁yerde 59188 ▁याच 59188 ▁ღვ 59188 ▁dorp 59187 ▁intero 59187 好好 59186 diw 59185 ▁Herria 59185 ▁தொடர்பு 59185 ▁Fif 59184 アクセス 59184 ▁Pàgina 59183 ▁ostal 59183 ▁нія 59183 ▁നാല 59183 נוס 59182 වක් 59182 ▁έχ 59182 ▁inus 59181 wuel 59180 ▁sasta 59180 tavan 59179 ▁nene 59179 ▁succ 59178 ▁Уста 59178 केट 59177 ▁salamat 59177 ▁tenta 59177 ▁مجله 59175 yku 59174 זט 59174 ▁dječ 59174 生日 59174 ставно 59169 ▁ገብ 59168 שרים 59167 ელში 59167 ▁audz 59167 भग 59166 ▁database 59166 sienio 59165 ▁மனை 59165 ahing 59164 หรียญ 59163 ▁Baden 59162 ▁İst 59162 ▁රතිචාර 59162 ınıza 59161 러스 59161 ▁uMnyango 59160 ▁umbes 59160 ▁գործըն 59160 ▁ถนน 59160 ▁tier 59158 седі 59157 ▁pink 59157 اريء 59155 ෆ් 59155 ▁løsninger 59155 apai 59154 gı 59154 ▁Важно 59153 ▁povo 59152 ▁tenke 59151 ▁zakaj 59150 ▁waith 59149 留学 59149 ັບປ 59148 ▁hverju 59148 ▁دهه 59148 ▁بينما 59147 ▁डाल 59147 lako 59146 ന്ത് 59146 ▁висока 59145 endiri 59144 ultima 59144 ▁。 59142 ▁Wszystkie 59141 ▁menemp 59141 ▁አይነት 59141 norm 59140 ▁сөнки 59140 atının 59139 ▁aloha 59139 odav 59138 ංග් 59137 ▁vietą 59137 ▁درخت 59137 ▁প্রেস 59137 ▁bised 59134 ▁propag 59134 ▁соб 59133 ষ্টা 59129 esperienza 59128 無理 59128 ▁અમેર 59127 3000 59126 λακ 59126 ▁02, 59126 ▁Semp 59126 かせ 59126 ของเขา 59125 ▁гаргасан 59124 だが 59124 ექტის 59123 ▁Suites 59123 ▁2,6 59122 ▁enví 59120 ▁canlynol 59119 ▁chiam 59119 ▁harmadik 59119 бурга 59118 ▁ບົດ 59118 ▁омогу 59117 ▁ଶିକ୍ଷା 59116 ▁падчас 59115 Afrikaanse 59114 ▁Cristiano 59113 ▁разговара 59112 ▁salvar 59111 זרת 59110 ▁förslag 59109 ▁تقویت 59109 が出て 59109 estima 59108 ▁Usa 59107 ▁stavu 59107 ▁ಹೆಸರು 59107 rance 59106 を読 59106 ▁fixa 59104 ▁лауреа 59103 ▁практы 59102 放送 59102 ▁lahayn 59101 ▁mücadele 59101 ▁förhåll 59100 ▁rydym 59100 ▁い 59100 ▁movement 59099 ▁paglago 59099 ▁ยา 59098 ▁pents 59097 ▁ඇත්තටම 59097 artesi 59096 ▁bunlar 59096 ▁conhec 59096 ▁zran 59093 ▁մտած 59093 ▁מחש 59093 ▁DAS 59092 ▁džia 59092 ▁ionn 59091 ▁stiet 59091 områder 59090 ▁ସାମ୍ 59090 藝術 59090 Lëtzebuer 59089 ▁kendini 59089 ▁devrait 59087 ñal 59084 ▁Congres 59084 ▁kastoy 59084 ρούς 59082 ٽين 59082 liad 59081 ποίη 59081 ▁Kristen 59081 rzeć 59080 ▁ifad 59080 ▁trondheim 59080 ▁нравится 59080 ▁организм 59080 hatja 59079 ▁υπε 59079 ▁विकल्प 59079 ייר 59078 ▁israel 59078 ▁býval 59077 ▁speciāl 59077 moniy 59076 ▁Sicher 59076 ▁ಅಕ್ಷ 59074 ▁Seda 59073 ▁alıb 59073 িটি 59072 ▁ďalších 59072 ▁sacara 59069 ચર 59068 ▁deberá 59068 ▁levant 59068 şıq 59067 ▁Zara 59067 ньор 59066 ▁putut 59066 σοκο 59062 ▁kvalifik 59061 ▁stero 59061 ▁vín 59061 ▁Крем 59061 ▁पदार्थ 59061 ▁ನ್ಯೂಸ್ 59061 ▁ontvangen 59060 ▁ഇന്ത്യന് 59059 ▁ימי 59058 ▁ريا 59058 90, 59057 ▁01, 59057 ▁архивланған 59057 ▁اللب 59057 örü 59053 ▁ಉದ್ದ 59052 almi 59051 ພົບ 59051 ▁phone 59051 ▁deciso 59050 ▁plur 59050 мнить 59049 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▁Rezult 58954 ▁recurso 58954 ▁створю 58954 ▁bezb 58953 ▁يعرف 58953 ลักษณะ 58951 ▁svjed 58951 ▁მარი 58951 ▁furasta 58949 ▁арттыру 58949 ▁өмн 58949 ▁Medan 58947 ংশ 58946 ▁berdi 58946 ▁eolas 58946 ▁Bentuk 58945 ▁pěk 58945 ▁អស់ 58945 klāt 58944 ົບຮ 58944 ちな 58944 orej 58943 мол 58943 بیه 58943 یرات 58943 ▁Sitz 58943 ▁huden 58943 ▁spalvos 58943 ▁컴퓨터 58943 ▁emocional 58942 ђује 58941 ▁galing 58941 ▁դոլ 58941 ▁مراکز 58941 ▁Overs 58939 ▁ਸਿਆ 58939 бақ 58938 ▁گذر 58937 ▁Jersey 58936 ▁נס 58936 Android 58935 тае 58935 ▁juist 58934 ▁Avui 58933 ▁funksion 58932 ▁oef 58932 hre 58930 ▁lumin 58930 第四 58930 æreste 58929 ▁pól 58929 mör 58927 ▁tietää 58927 合格 58926 ▁odda 58925 大力 58925 لاک 58924 ▁1.500 58924 ▁τεχνο 58924 ürlich 58923 ാവുന്ന 58923 ▁oyunu 58923 ▁რადი 58923 就有 58923 ▁stöð 58922 saus 58920 ▁Weitere 58920 éngs 58918 ემბერი 58918 ▁ઉમે 58917 ▁всему 58914 ▁ពលរដ្ឋ 58914 ▁vrst 58912 ▁zilei 58912 ▁Вона 58912 ▁администрации 58911 ▁мисля 58910 ............ 58909 લિક 58909 ▁combusti 58909 ண்ட் 58907 ▁konpr 58907 ▁aloq 58903 ţională 58902 ▁iwu 58902 ▁tayyorlash 58902 ▁કવિ 58902 ▁comfort 58899 աթիվ 58897 jike 58896 ▁dyst 58895 ▁pekerja 58895 ▁البشر 58894 ▁૨૦૧ 58894 istii 58893 مم 58893 ▁materiálu 58893 ▁servidor 58893 ▁دير 58893 ▁kimataifa 58892 tezza 58891 ühren 58891 ▁anom 58891 ▁kii 58891 ▁pelikula 58890 ▁урыҫ 58890 ▁देउवा 58890 alikan 58889 ▁уд 58889 ohitra 58888 يلي 58888 ▁ਘੱਟ 58888 ▁Дэ 58887 のこと 58887 ▁아니다 58886 '" 58885 ▁заходи 58885 โปรโมชั่น 58884 ▁eğlen 58884 ▁graves 58884 ▁hapur 58883 ▁використовувати 58883 ▁укла 58882 ▁хле 58882 ▁קאר 58881 მენ 58880 ▁בעיר 58880 ▁اوسه 58879 ▁unica 58877 ▁сохрани 58877 ▁وبين 58877 ▁konven 58876 ▁1990, 58875 ▁Iya 58875 융 58874 ▁municipio 58873 ▁городской 58872 йыр 58871 amanye 58870 ▁tecnica 58868 ▁ហេតុ 58868 itteet 58867 ▁ameste 58867 teis 58866 မို 58866 ገኛ 58866 ▁vastavalt 58866 ▁سلطان 58866 ▁програмата 58865 łożyć 58864 ▁έχεις 58864 ▁աճ 58863 োয় 58862 ຊາວ 58862 ▁Trabalho 58862 ojatno 58861 жности 58860 ▁일정 58860 ▁contenuti 58859 ▁politisk 58859 也没有 58859 brit 58856 janju 58856 ▁մարմին 58856 ▁বুধবার 58856 ▁የገ 58856 ▁регион 58855 ▁ನಾಲ್ 58855 ▁територі 58854 ▁haaste 58853 ▁ulus 58851 konn 58850 нову 58850 ▁Hati 58850 ▁dast 58850 ေနတဲ့ 58849 ▁пожела 58849 被害 58849 ଭବ 58848 ▁һәләк 58848 etoim 58847 ▁voiture 58847 ▁ਰਾਸ਼ 58847 ▁SBO 58846 ▁ngwag 58846 ▁ଅର୍ଥ 58846 INTA 58845 ▁Occident 58844 ائرة 58842 ▁Tram 58841 ▁обслужива 58841 ▁सिद्ध 58841 労 58841 ▁rektor 58840 ▁tammikuuta 58840 ▁надвор 58839 ▁ගුණ 58839 ▁duga 58838 ▁Performance 58836 ▁народження 58836 ▁თვის 58836 venta 58835 тыі 58835 ▁Kierch 58835 ▁мосол 58834 odyti 58833 uhe 58833 ▁dimanche 58833 ▁mwingine 58833 ▁çünkü 58833 ▁миру 58833 ▁استع 58833 ▁Уи 58832 ▁כנ 58832 זיק 58831 ▁Bonjour 58831 ▁رن 58831 нымен 58830 unei 58829 ▁çap 58829 ປະກອບ 58826 ൂരി 58825 ▁antsika 58825 ▁nabí 58825 ▁υπόλοι 58824 ▁Mihai 58822 ▁daitez 58821 ▁kungak 58819 ▁shit 58819 ▁tenia 58819 ▁آزمایش 58819 ▁같다 58818 linya 58816 ulaan 58816 ▁Grecia 58816 ▁Statistik 58816 ▁สุด 58816 ដឹកនាំ 58815 ▁ক্ল 58815 赌 58815 ▁kasnije 58814 ▁ଅଞ୍ଚ 58814 ▁الإلكتر 58812 ТІ 58811 ▁mbyll 58811 ▁Потре 58811 सत 58810 ற்கொ 58810 ▁مناف 58810 derdag 58809 ୃତି 58809 ▁답변 58809 教えて 58809 ላማዊ 58807 ▁comportamento 58807 prochen 58806 ▁babi 58806 ▁სერი 58806 ídas 58805 äg 58804 ▁Lekin 58804 ▁Minä 58803 ▁dévelop 58803 ▁ที่มี 58803 ▁password 58802 eldo 58801 ▁Spezi 58801 แข็งแรง 58800 ▁lô 58800 とお 58800 疾 58799 ▁Osman 58797 ▁массо 58796 bile 58793 גרי 58793 adee 58791 uć 58791 ▁04, 58791 ▁kuno 58791 ▁tyto 58791 แตก 58788 ционные 58787 ьер 58787 ▁kedven 58787 ▁ի՞նչ 58787 ▁ବିଧ 58786 ▁Reinu 58785 cześnie 58784 ▁perif 58784 ▁väx 58784 gär 58783 ▁dore 58783 ▁ebb 58783 ▁ينت 58783 ▁طرز 58782 isering 58781 xaaf 58781 ▁ಸುಮಾರು 58781 xsh 58780 ▁duc 58780 ▁jakość 58780 ▁miaraka 58780 ▁वरिष्ठ 58780 ฝรั่ง 58779 ▁ማኅ 58779 미터 58779 서를 58779 ▁sobe 58778 ļaut 58777 ▁personenbezog 58777 ▁pedra 58776 éig 58775 ▁Shin 58772 ▁וכי 58771 ▁ଦିବ 58771 ▁moƴƴ 58770 orixinal 58769 ritte 58769 eldud 58767 ▁Otra 58766 ødt 58765 ▁ផ្ល 58765 ▁ვინც 58764 ▁rare 58763 ▁сургалт 58762 ճար 58761 ottu 58760 ▁Fél 58760 ▁повідомляє 58760 ▁ಚೆನ್ನ 58760 ▁정상 58760 ▁हरेक 58759 пев 58758 ধা 58758 ▁තිබූ 58758 ▁ഒഴിവാ 58757 ▁Thư 58755 ▁хвата 58753 ▁istəyən 58750 ▁kwayo 58750 ▁poświę 58750 ▁підтвер 58749 在这里 58749 ▁Eir 58748 ▁Grill 58748 ▁kear 58748 ավորում 58746 କରେ 58745 ▁zomb 58744 ▁प्रतिनिध 58744 ▁ljet 58743 ▁دادند 58741 ▁قربانی 58741 म्यान 58740 ෙනි 58740 ▁Chuo 58739 ▁ښاغلي 58739 ▁यांचे 58739 ▁붙 58739 פרס 58738 ワー 58738 ▁helyett 58737 ciół 58734 yllä 58734 ▁Shari 58732 ▁ondernem 58731 ▁बलात्कार 58730 ▁প্রকাশিত 58730 ▁evrov 58729 ▁souhaitez 58728 ▁शौ 58728 ▁ಕೇವಲ 58728 ývá 58727 ▁репрезента 58727 Action 58726 ▁ಪತ್ರ 58726 דרות 58724 קאַ 58724 ▁הצד 58724 ▁스트 58724 ueel 58723 ▁benotzen 58723 ▁filha 58723 ন্ত্রী 58722 ▁بارش 58721 ▁አጥ 58721 sad 58720 ▁nuolat 58720 волу 58718 ထင္ 58718 ▁1989, 58718 ▁хая 58718 ▁وڪ 58718 raph 58716 ▁отношений 58716 บก 58714 ▁breast 58714 ▁igår 58714 ▁terwijl 58714 ਭਾਵ 58712 ▁алты 58712 尊重 58712 ▁प्रतिक 58710 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58655 ▁داشتند 58654 ələrdən 58653 ሶች 58653 ▁ricord 58652 ▁განვითარების 58652 ▁ვებ 58652 hden 58650 ▁garn 58650 ▁кожного 58650 ▁combate 58649 ▁educação 58649 itwa 58648 勧 58648 ▁Alas 58647 ▁nenpòt 58647 ▁desper 58646 ▁สํานักงาน 58645 ▁hizkunt 58644 ότερο 58643 ▁Ear 58643 gangen 58641 ประสิทธิภาพ 58641 ▁Arī 58640 ▁начать 58640 үрэн 58639 ▁ange 58639 ▁rét 58639 ▁ბევ 58639 νούν 58638 הר 58638 ▁atụ 58638 ▁แบ 58638 azen 58637 ▁recommand 58637 ▁Шве 58636 ▁गिर 58636 ▁ფეხ 58636 リスト 58636 處理 58636 urken 58635 ▁grij 58633 aktioun 58631 ifuna 58631 ▁tarda 58630 わた 58630 αισ 58629 ▁encanta 58629 icit 58628 ▁Cumhurbaşkanı 58628 ▁joven 58628 bix 58627 elked 58627 ▁jazdy 58626 ალიზ 58625 шысы 58624 하신 58623 記者 58622 ▁दर्ज 58621 केका 58620 ▁Krista 58620 ▁عبادت 58620 גד 58619 ▁bloga 58619 ▁vintage 58619 ▁ግንኙነት 58619 ▁apartament 58618 ▁تجهیزات 58618 ilmektedir 58617 ▁тәрби 58617 ▁Dnev 58616 ▁İkin 58615 博物 58615 BT 58614 gano 58613 wahi 58613 dess 58612 ▁psycholog 58612 ▁οπ 58612 ▁जवा 58612 udal 58611 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プログ 58566 ▁मॉ 58565 ▁व्याप 58564 資格 58564 ғанын 58563 ▁स्त्री 58563 dant 58562 ensdag 58562 esema 58562 rtas 58562 ▁Deci 58562 ▁غونډ 58562 ▁apertura 58561 ▁chemin 58559 ▁মানুষের 58559 ▁տակ 58558 ▁ფეხბურ 58557 глу 58556 ▁emberi 58556 ▁siarad 58556 ikų 58554 ▁प्रचार 58554 ▁දේශපාල 58554 安装 58553 ▁გვერ 58552 qiqot 58550 ▁alig 58550 ▁biur 58550 ▁सम्बन्धित 58550 izuar 58549 ▁AMD 58549 ▁afirmou 58549 ▁کورنۍ 58549 ปป 58548 ▁jednotlivých 58547 ▁требует 58547 ▁ҡушы 58547 bayar 58546 ▁Konst 58546 ▁cikupa 58546 եղաս 58545 ▁xay 58545 ոնը 58544 ▁কল 58544 iców 58543 ให้คุณ 58543 ▁ଲଗ 58543 ▁union 58542 ïo 58541 рел 58541 ▁Ρω 58541 ▁आपल्याला 58541 ehör 58540 에서의 58540 ▁tarkoit 58539 ▁accompagn 58538 alea 58537 资格 58537 로는 58537 ▁CAN 58536 ▁nepot 58536 ▁rung 58536 ▁الكه 58536 teborg 58533 ▁გათ 58533 árny 58532 ▁방식 58532 ▁limb 58531 ▁Κυριακή 58530 便宜 58530 ▁جائے۔ 58529 peng 58528 qon 58528 ▁ಸಲ್ಲಿಸ 58528 ▁igri 58526 ▁ngenye 58526 isuuksia 58525 ▁inspek 58525 ▁peric 58524 associa 58523 wujudkan 58523 ▁Martínez 58523 ထဲမွာ 58522 issença 58521 ρησης 58521 ▁dahilan 58521 fm 58520 ▁provoca 58520 ▁doty 58519 ▁এটি 58519 ▁বিপ 58519 enhagen 58518 חם 58518 inanana 58517 សង្ស័យ 58516 ▁instituciones 58516 ▁ελέγ 58516 ▁लव 58516 ▁galle 58515 ▁今回 58515 θύ 58514 σέ 58514 ▁sanajan 58514 ▁nyíl 58513 ландыру 58512 ▁המכ 58512 ровал 58511 ▁ISSN 58511 ▁oled 58509 ▁sàng 58509 ▁annivers 58508 eloos 58507 opan 58507 ▁Menjadi 58507 ▁tekur 58507 ▁приходится 58507 ٹوں 58506 ▁ruchu 58506 ባብ 58505 ▁pavad 58505 intų 58504 ▁culpa 58504 edag 58503 باع 58503 ▁cifra 58503 °, 58502 یدہ 58501 ▁материа 58500 oficial 58499 ▁ಪಾತ್ರ 58499 Fre 58498 ▁Roland 58497 ▁төхөөрөм 58497 Ű 58496 ▁जम्मू 58496 ▁Հայաստանը 58495 ▁maisto 58494 ▁яким 58494 ▁ખરી 58493 rui 58492 ▁Apps 58492 ▁badania 58492 şağı 58491 ▁Sõ 58490 leiding 58489 ▁3,0 58489 永远 58489 ന്ത്ര 58488 ▁मदत 58487 betaling 58485 bahn 58484 😊 58484 ▁inversión 58482 ▁scenari 58481 ▁ዩኒቨ 58481 emplar 58480 ▁dhibaato 58480 ▁obci 58479 伏 58479 ▁кастрычні 58477 islamu 58476 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58423 ▁prikl 58423 ▁режиме 58423 럴 58423 behör 58422 ദം 58422 ▁Παιδ 58422 vento 58419 ▁உடல் 58419 джан 58418 ብኝ 58418 ▁гипер 58418 ▁ফেব্র 58418 ▁Laptop 58416 chein 58414 ▁भण्ड 58414 ไกล 58413 投资者 58413 aroon 58412 словно 58412 ▁demokratik 58412 өгийн 58411 ▁puheen 58410 ▁itibaren 58409 ▁mengajar 58409 らせ 58409 ▁términu 58408 ▁tribunal 58407 ▁ārst 58407 ▁سپاه 58407 ▁செயல் 58407 スキ 58407 ወሓት 58406 ▁duże 58405 ▁பாதி 58405 shen 58404 ুরী 58404 普段 58403 ួសារ 58402 ▁baad 58402 ▁fatta 58402 ▁држави 58402 yszerű 58401 ▁Buah 58401 IKAN 58400 æde 58400 vac 58399 ▁maintsy 58399 ▁դիր 58399 edur 58398 ▁ஒரே 58396 aer 58392 प्रा 58392 ▁inlägget 58392 ▁ùir 58392 eguir 58391 الع 58391 ▁Yàlla 58391 muan 58390 ekisa 58388 ▁далей 58386 ultural 58383 ▁ٹیکنالوجی 58383 筹 58382 ▁vry 58380 ▁ہمیشہ 58380 ▁absolv 58379 ▁awdur 58379 ▁Leip 58378 ▁siúl 58378 मक 58377 ീം 58377 ▁Continu 58377 ▁سگهجي 58377 accès 58376 ▁muncho 58376 ▁რათა 58375 ենան 58374 ▁उम्र 58373 »). 58372 ▁60, 58372 ▁міндет 58372 ▁भएकाले 58372 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▁نگران 58323 ▁хиля 58322 ▁lenni 58321 ▁Аза 58321 ▁دعو 58321 ▁উত্ত 58321 ▁1:1 58320 ▁مواجه 58319 abana 58317 ▁резо 58316 ▁саҡта 58316 ▁qəs 58315 ▁Επισ 58315 ▁بالع 58314 улт 58311 ▁көлемі 58311 ▁??. 58310 ▁saída 58310 шә 58309 جھ 58309 stād 58308 ▁Glob 58308 ▁ስል 58308 ▁වෙන් 58306 個人情報 58305 감을 58303 ▁BV 58301 ▁åka 58301 ▁başçı 58300 ▁ਡੇ 58300 ▁ਸਾਨੂੰ 58300 2003 58299 ▁Aro 58299 ▁لڑکی 58297 Pod 58296 aline 58296 ▁Cei 58296 ▁تعط 58296 ▁Jaya 58295 ▁apu 58295 ▁nadzieję 58295 ▁инова 58294 KW 58293 ▁2022 58293 ▁vahet 58292 ▁mój 58291 ▁грамо 58291 ▁opciones 58290 ▁Херце 58290 ▁скачать 58290 jisë 58289 ▁позволит 58289 qsa 58288 ▁სრულ 58288 ália 58287 ▁장소 58287 словен 58286 ▁алтерна 58286 ▁jauna 58285 Мә 58283 ไปได้ 58283 ▁Philippe 58283 ▁bintang 58282 ▁지나 58282 diak 58280 ▁Возможно 58280 ▁انتہائی 58280 yiz 58279 മം 58279 ▁утвержда 58279 ▁BOS 58278 皆さん 58278 ▁Үҙәк 58277 hetnek 58276 ▁գործընթաց 58276 ▁မ်ား 58276 ântul 58275 ▁Címkék 58275 ▁salva 58275 ก็ตาม 58274 ▁diyor 58274 ▁informar 58274 ▁wink 58274 ▁өлкә 58273 ເປີດ 58272 ▁صدار 58272 стове 58271 ▁ବ୍ୟାଙ୍କ 58271 ▁samling 58269 ▁ሄ 58269 Asia 58268 ტად 58268 ▁ανθρώπους 58268 様子 58268 ▁maksimum 58266 izzato 58265 парт 58265 ▁Unternehmens 58265 ▁uvjet 58265 ▁sebep 58264 本人 58264 многу 58263 achan 58262 ▁Hamm 58262 ▁അതിന്റെ 58262 шката 58261 ▁nedir 58261 ▁obitelj 58261 логе 58260 ▁Ghi 58260 ▁atëherë 58260 ▁ಮಂಗಳ 58259 alaí 58258 catori 58258 cyjny 58258 hausen 58256 Пре 58255 ▁คณะ 58255 ▁thác 58254 ▁લઇ 58254 ▁በረ 58254 ithiol 58253 ▁referencies 58253 جين 58252 ▁Situation 58251 ▁hovor 58251 ▁अन्तर 58250 uble 58249 ▁Johnny 58249 ▁процеси 58249 ▁גרו 58249 ▁omista 58248 rassa 58246 וחים 58246 ▁Hamilton 58246 ▁граница 58246 ▁Aa 58245 ▁ցույց 58244 ▁ಕ್ರಮ 58244 јање 58243 alaiset 58241 ▁τίποτα 58241 ▁якое 58241 提案 58241 ▁Wor 58240 ▁rhaglen 58240 ▁ഒന്ന് 58240 kār 58239 саулық 58238 yik 58237 უმის 58237 ▁dựa 58237 ▁führt 58237 ▁përf 58237 ▁župan 58237 oting 58236 inys 58235 лово 58235 ▁femeie 58235 ▁Երեւանում 58235 ▁معرف 58235 ▁ໄວ 58234 ansson 58233 ▁domhain 58233 arengan 58232 ▁planer 58232 ▁утре 58232 เด็จพระ 58230 ေျမာက္ 58229 ▁oblast 58229 ▁yt 58229 ▁إضافة 58229 ▁घरेल 58229 მაღ 58227 ▁réuss 58227 ▁Frag 58226 ▁zauj 58226 ▁Amikor 58225 ▁endlich 58225 ართა 58224 ▁ezinkulu 58224 ▁ອີກ 58224 ▁Gat 58223 ▁светов 58223 IZA 58222 ▁დახმარ 58222 ள்ளை 58220 ▁Донба 58220 ▁লেখ 58220 ▁ಪಡೆ 58220 ▁formação 58219 ▁بالاتر 58219 ▁நிகழ் 58218 ràn 58217 กรา 58217 ▁νερό 58217 גלי 58216 ▁উই 58216 לך 58215 ▁sgrìobh 58213 ▁anzi 58212 ▁загру 58212 ▁ಪಂಚ 58212 ライブ 58212 agaa 58211 funeka 58210 stedet 58210 ிருக்கும் 58210 ▁Dësen 58209 ▁2/ 58206 ▁borav 58206 ▁ګو 58206 iscint 58204 ▁föruts 58204 ▁teszi 58204 ▁នូវ 58204 ▁oposición 58203 ▁ребенок 58203 ▁хриш 58203 bour 58201 няване 58201 ▁пройти 58201 ▁ရဲ 58201 влений 58199 ▁hieronta 58199 ▁осущест 58199 ▁סג 58199 ▁ಇದರ 58199 UKO 58198 اعت 58198 ▁qëll 58198 ▁tuba 58198 endek 58197 ieszcz 58197 ▁rév 58197 ΚΕ 58196 ▁έφ 58196 ▁birkaç 58195 ▁település 58195 ▁vendas 58194 ▁படிக்க 58194 ▁Madagasikara 58192 улдаг 58191 ענק 58189 ▁contrari 58189 ίνα 58188 ▁opor 58188 ▁ලස්සන 58188 დიდა 58187 ከታ 58187 andığı 58186 ısına 58186 ▁frakt 58186 ▁Галоў 58186 ▁ይህም 58186 ūtas 58185 ഭ്യാ 58185 ▁köp 58185 ▁حصو 58180 ▁tsela 58179 itelje 58178 кос 58177 แว 58177 स्रो 58176 ▁dacks 58175 aquela 58174 ▁ажиллагааг 58174 slam 58173 ▁hoga 58173 ▁ský 58173 ขว 58172 ▁chamado 58172 ▁KOS 58170 ที่ผ่านมา 58169 ▁dilay 58169 ջոց 58168 ▁családi 58168 ▁свог 58168 esim 58167 ▁fakat 58166 場合には 58166 лээд 58165 rièr 58164 ▁Joka 58164 ▁filosofia 58164 ▁липня 58163 ▁ಮೈಸ 58163 ▁குழு 58160 ▁Elo 58159 ▁MOB 58159 ▁zaštitu 58159 999 58158 ▁буџ 58158 ▁הראש 58158 ▁ਸਾਂਝਾ 58158 ▁manus 58157 ▁lety 58156 ▁oferi 58156 ▁podatki 58155 ▁edy 58154 ▁Ανά 58154 羽 58154 ላን 58153 ▁pethau 58153 μένει 58152 ▁Challenge 58152 ologa 58151 ເຂ 58151 ▁seua 58151 ▁утица 58151 ▁결국 58151 بري 58150 adore 58149 elix 58149 allisen 58147 ▁पुग 58147 ▁تېر 58146 ▁ပို 58146 ▁prezzi 58143 ▁тәжірибе 58141 ▁bọn 58140 ▁sintet 58140 ▁такими 58140 ▁напада 58138 妊 58137 omuntu 58136 ąp 58136 ▁خوبصورت 58136 ▁pakub 58134 ▁експери 58134 实力 58134 ▁کنه 58133 ▁അവർ 58133 ▁Nk 58132 itorii 58131 ЛС 58131 тамі 58131 ▁forsø 58130 ▁gouzout 58130 administr 58129 ោន 58129 ısıyla 58128 ÷ 58127 ▁hlavní 58127 ▁LC 58126 ▁passada 58126 ▁һора 58126 ▁स्म 58126 ecký 58125 ▁terça 58125 yc 58123 ▁Prins 58123 ▁começ 58123 isuudet 58122 ▁printr 58122 жливо 58121 ▁kimse 58121 ▁μόλις 58121 ▁اشار 58121 ▁गांव 58121 ▁competencia 58120 ▁Alemanya 58119 lerne 58118 ▁ваших 58117 ▁kór 58116 ▁غږ 58116 ▁pinal 58115 ▁жыць 58115 ▁تصم 58114 ติดตั้ง 58113 ▁dalg 58113 ▁Ush 58112 була 58110 ▁detrás 58110 ▁tă 58110 בוצ 58109 ▁hirdet 58108 ▁Εργ 58108 ▁найкра 58108 ▁ವರ್ಷದ 58108 ▁febbraio 58107 ▁покуша 58107 ▁ଆଲୋଚନା 58107 ikyti 58106 ஜ் 58106 ▁श्रे 58106 ▁መልካም 58105 ▁Perlu 58104 چو 58103 ▁biridir 58103 ເດືອນ 58102 ▁једи 58102 ケーション 58102 inera 58101 ▁Abe 58100 atika 58099 ▁sjón 58098 ▁քաղաքացին 58098 ▁اولاد 58097 ▁نویس 58097 ▁Unik 58095 ▁läg 58094 поли 58093 废 58093 yslu 58092 ползва 58092 ଗ୍ 58092 ▁evangel 58092 ▁συμβα 58092 образова 58091 ▁összeg 58091 ▁објавље 58091 tritt 58088 ▁Йә 58088 ▁পো 58088 itako 58087 ▁Money 58087 ▁dhinaca 58087 意味着 58087 ▁uppdrag 58086 専 58086 ▁otel 58085 ▁परत 58085 สึก 58084 ▁руками 58084 ▁helmikuuta 58082 沖 58082 ▁domino 58081 ▁interneto 58081 vér 58080 ▁იდე 58079 ▁museo 58078 质的 58078 ଯୁ 58077 แพร 58077 рген 58076 ▁Všeobecné 58076 harmon 58075 ▁spins 58075 ▁terting 58074 ክርስቲ 58073 ▁यात 58073 ਕਾਰੀ 58072 ▁musste 58072 ▁Megj 58071 ▁køkken 58071 ▁الجنوب 58071 ▁Osta 58070 ▁tuul 58069 čnosť 58068 ▁Palest 58068 ▁designer 58068 ▁maravil 58068 ▁Secretaria 58067 ▁സാമ 58067 ▁WIB 58066 ▁Светог 58066 ▁заједно 58066 ▁міст 58066 ▁веще 58065 芸 58065 kade 58063 əbi 58063 ▁follow 58063 ▁partager 58063 ▁тыу 58063 ▁kahe 58060 ingizimu 58059 ▁süste 58059 ▁Musul 58058 ▁bier 58057 ontrol 58056 ▁fű 58056 ▁مری 58056 ▁ಬಹು 58056 所谓 58056 ▁përfshirë 58055 ▁тексту 58055 ЭД 58054 ▁amelyet 58054 ▁υποχρε 58054 ▁ڪرائي 58054 েছে 58053 ▁Hafðu 58053 emise 58051 inizio 58051 ▁spring 58051 eteen 58050 ▁Asocia 58050 ▁алғашқы 58050 တိုက္ 58049 ▁beskikbaar 58049 ▁fela 58049 elyik 58046 žaj 58046 ▁ɗa 58046 ▁ପ୍ରଦାନ 58046 ▁බේ 58046 ▁imatge 58045 islam 58044 ▁commande 58043 ▁ڊيسڪ 58043 ▁ალბათ 58043 сою 58041 ▁campe 58041 他说 58041 ြမ္း 58040 ▁pareig 58040 ▁dóibh 58039 ▁peningkatan 58038 stöðu 58037 ▁Split 58037 ▁rde 58037 ▁فری 58037 ▁kurrë 58036 lion 58034 জ্ঞ 58034 ▁drech 58034 ▁laquelle 58033 ▁Lorenzo 58031 ▁Lla 58030 办公室 58030 ▁sociedá 58029 ؤون 58028 ▁addım 58028 ▁esetében 58028 полити 58027 ጠብ 58026 ▁Administ 58026 ▁dokonca 58026 acaktır 58025 लित 58025 ▁звани 58025 ▁यस्ता 58025 ოლა 58024 ڻڻ 58023 ▁ifuna 58022 စံ 58021 ▁dyl 58021 ▁fev 58021 ▁महानगर 58021 ▁ရဲ႕ 58020 ippu 58018 드로 58018 ▁mede 58016 ▁йәр 58016 ეული 58015 ▁Melaka 58015 ▁مرحوم 58015 ▁পরিষ 58015 varos 58013 ▁Ow 58013 ೀಪ 58011 ▁našu 58011 땅 58011 ercayaan 58010 ▁sonraí 58010 ▁Вучић 58010 ▁înţ 58008 nikazi 58007 ▁appare 58006 ▁realizadas 58006 ▁skand 58006 ელთა 58005 ិ៍ 58005 onesia 58004 ▁subir 58004 ▁автомат 58004 ▁бассейны 58002 ▁مٿان 58001 ▁MEG 57998 ▁Rent 57998 hatag 57997 Ca 57996 ▁2,2 57996 ▁eficiente 57996 ▁મંદિ 57996 始终 57996 ର୍ଷ 57995 ugeot 57994 غب 57994 ▁رهن 57991 ▁Ideen 57990 ched 57989 ològ 57989 บวน 57989 ▁μπροστά 57989 ▁six 57988 ▁грамадзян 57987 क्षात 57985 ▁ಬೇರೆ 57984 ▁있게 57984 ▁مختلفة 57983 akaw 57982 сум 57982 ▁pracuje 57982 ▁lapse 57981 ▁Дет 57981 ▁مهارت 57981 ಾಯಕ 57979 inayon 57978 zeiten 57978 ▁แฟ 57978 ▁самостій 57977 valits 57975 əzər 57974 ▁izs 57973 ▁захва 57973 ▁прожива 57973 ღვე 57972 ▁Sən 57972 ▁bjó 57972 પરા 57971 ▁gabh 57971 ▁Ийм 57971 ▁ചര് 57971 佩 57971 ukatan 57969 ▁lão 57969 ▁બંને 57969 لیں 57968 लाल 57968 магази 57967 ਧਾਰ 57967 ▁niño 57967 ▁бухгалтер 57966 ▁anderem 57965 HC 57964 rosi 57964 скар 57961 ▁devr 57961 ▁اسد 57961 ײַן 57960 ▁After 57958 ▁compens 57958 ▁famoso 57958 ▁nebija 57958 ▁podstat 57958 elset 57957 ▁reten 57957 ihlás 57956 igwa 57954 ▁Använd 57953 ▁destas 57953 ▁الداخلية 57953 ▁जाये 57952 ▁OROM 57950 ▁negra 57949 ▁Login 57948 ▁гаргаж 57948 性格 57948 ปรา 57946 ▁įstat 57946 ▁Сою 57946 ▁پنھنجي 57946 ▁सांगितले 57946 ▁პარლამ 57945 ▁Elke 57944 ▁dotyczące 57944 很大的 57944 anann 57943 hood 57943 olli 57942 вною 57942 ▁Policia 57942 kår 57941 αιν 57940 ▁origina 57939 ▁będę 57938 ▁فرانسه 57937 éngscht 57936 ▁membro 57936 ▁Սիր 57936 漫画 57936 亚洲 57935 ▁åpen 57934 ▁підприємств 57933 ▁Rosen 57931 ▁plač 57931 会有 57931 וחר 57930 ▁iechyd 57928 ▁पौडेल 57926 ଷ୍ଟ୍ର 57923 edinte 57922 ▁Boys 57921 ▁kova 57921 kað 57920 גוסט 57920 ▁ಗಾಂ 57920 ▁αυτόν 57918 ▁асабліва 57917 ▁совершен 57917 ▁целиот 57916 ▁temperature 57915 ▁кәсіпкер 57915 戒 57914 ▁තෝ 57912 ▁ေန႔စဥ္ 57911 akang 57910 ▁eventuell 57909 ▁લોક 57909 러운 57909 ▁мәҡәләне 57908 17, 57907 âni 57907 ▁žur 57907 ▁журналы 57907 eraa 57906 ▁اجرایی 57905 itzaren 57904 odaidh 57904 كيد 57904 ▁carita 57904 ▁früh 57904 ▁περιλαμβ 57904 szem 57903 ▁Категория 57903 ਬੀਰ 57902 ▁soni 57901 ▁канди 57900 动力 57899 aminan 57898 ▁dünyaya 57898 ▁izpol 57898 леү 57896 ▁Меѓу 57895 ▁சேர்ந்த 57895 ▁olmadığını 57894 ▁Dest 57893 つも 57893 vís 57892 ▁bavame 57892 ▁gross 57891 ▁Seura 57890 ▁ayelujara 57890 ▁cabe 57889 ▁storico 57887 ▁DIR 57885 ▁сына 57885 ungis 57884 пря 57884 ▁Хууль 57884 ▁ລ້ານ 57884 тит 57883 慰 57883 ▁dhimma 57882 ▁полиция 57882 घर 57881 ▁læs 57881 国务院 57878 充满 57877 ▁artistes 57876 ▁zadr 57876 ▁ወያኔ 57876 ▁المرح 57875 ▁2-4 57874 ▁ሺህ 57873 בלה 57872 ચે 57872 ▁assicur 57872 ▁godziny 57872 ▁Хер 57872 ▁برند 57872 ГИЙН 57871 ▁choisir 57871 ▁luòc 57871 werten 57870 ède 57870 ខែ 57870 ▁देखने 57870 ▁ඊයේ 57867 ▁spons 57864 ▁सर्वोच्च 57864 ahlen 57863 ▁Rép 57863 cipais 57861 ▁webs 57860 ▁өргөн 57860 的美 57860 τιν 57859 ▁hetk 57859 愈 57859 ▁Abdull 57858 ▁mismos 57857 ▁ଠାରୁ 57856 ▁načrt 57855 izmati 57854 menet 57854 ികളും 57854 ▁poca 57854 ▁viņiem 57854 িকেট 57852 ▁Παν 57851 ▁კომპ 57851 მას 57849 ▁Suzuki 57848 成的 57848 κλή 57846 ▁مهمان 57846 uthiwa 57844 দার 57844 ▁vaikuttaa 57844 ▁omaa 57843 ▁отримати 57842 ▁უცხო 57842 ▁танта 57840 ▁মার্চ 57840 ▁pikku 57838 ▁Подроб 57838 撞 57838 ávat 57836 ▁aze 57836 ▁hätten 57836 ▁להס 57836 стити 57835 ▁Ande 57834 ▁eas 57834 ▁great 57834 ▁maqsadida 57834 ▁сервис 57833 ▁animali 57832 ାଇଲେ 57831 ▁milih 57831 ▁Prest 57830 ▁necessidade 57830 ▁teoría 57830 щихся 57829 ▁Wine 57829 ▁ರಿಯ 57829 ▁ESC 57828 ▁آمار 57828 egov 57827 ชัน 57827 ▁Лат 57827 ڑوں 57826 လေး 57824 пенно 57823 nhet 57822 äitlech 57822 úi 57822 ხრ 57822 ▁удобно 57821 5000 57820 ▁Zentrum 57819 ▁móti 57819 ▁тұрғы 57819 çilik 57818 ченка 57818 ▁šalt 57818 인다 57818 isial 57816 แตกต่าง 57816 ▁hanter 57816 ▁мянган 57814 ▁номина 57814 ▁دارو 57814 凝 57814 ▁halkaas 57812 ▁possiamo 57812 nyddoedd 57811 ▁жөн 57811 ▁засно 57809 ▁Duc 57808 ▁bëri 57808 ▁negro 57808 ▁տարեկան 57808 եին 57806 ਧਾਨ 57806 ▁yıllık 57806 ▁دبیر 57806 jelu 57805 ▁boot 57805 ▁itiner 57803 ▁Хор 57803 ▁ensuite 57802 ວິດ 57801 ▁Fans 57800 在中国 57800 ▁ಹಲವು 57799 ▁Tiêu 57798 ▁prata 57797 ウン 57797 贯彻 57797 12.201 57796 ීමෙන් 57796 ▁aslında 57795 arım 57794 ▁ndjek 57794 ▁любого 57794 ▁faru 57793 сматри 57789 ▁cyw 57789 ▁ოც 57789 ▁bəyan 57788 ▁переві 57788 ▁მანქ 57788 ▁એવા 57784 ▁undersøg 57783 ▁Cyr 57782 ▁deyir 57782 ▁erfaringer 57782 ▁rappresenta 57782 ampaikan 57781 ▁معه 57781 ▁نیمه 57781 렵 57781 ▁hakimiyyət 57779 ▁къде 57779 ▁Бре 57778 ▁متعدد 57778 ▁Certific 57777 ▁bestäm 57777 ▁szór 57776 ▁perfe 57775 ▁testoster 57775 ▁БУ 57774 ▁нему 57774 лырға 57773 ▁cuesti 57773 ▁träning 57773 ▁τελικά 57773 ▁чувству 57773 ▁kritis 57772 ▁тулыһынса 57771 ▁Masch 57770 saada 57768 ٽس 57768 ▁mało 57768 ▁מבחי 57767 ▁ndrysh 57766 kozik 57765 ▁Szolgáltat 57763 īvs 57762 ▁कृपया 57762 retot 57761 ▁اشتباه 57761 lobal 57760 noloji 57760 ▁javnost 57760 ▁leschte 57760 ကေလး 57758 ▁наго 57758 ▁uyar 57757 ▁picio 57756 itorial 57755 oweit 57755 ▁Atu 57755 štine 57754 νοια 57754 ▁личност 57754 طرة 57751 ଜ୍ଞ 57751 ▁nakak 57751 horn 57749 කරන 57749 ▁hensyn 57749 meņa 57748 ▁קונ 57748 18] 57747 raya 57747 ԱԳ 57746 国の 57746 ڻين 57745 ▁Cambodia 57745 ▁படை 57745 ▁protek 57744 ▁soutě 57743 佐 57741 мель 57739 രാജ 57738 ▁plugin 57738 ▁екенін 57738 ▁состоит 57738 แก้ไข 57737 előtt 57736 ▁রাষ্ট্র 57735 ▁olleet 57734 ▁vendredi 57734 ▁vèr 57734 ▁инци 57733 ▁क्लब 57732 स्थल 57731 ▁جیت 57731 ▁líf 57730 ▁hoja 57729 ▁þing 57729 ▁Energia 57728 と呼 57728 bagai 57726 ▁туҡта 57726 lica 57724 žinti 57724 ▁permettre 57724 ▁آدمی 57724 อร์ท 57723 ▁apoy 57723 ▁kuona 57722 क्रमा 57721 நாடு 57720 ▁krā 57720 ▁situasi 57720 ▁Таны 57720 ▁changamoto 57719 ▁sary 57718 ▁автомобил 57718 ▁muzi 57717 ▁иһә 57717 ▁مسابقات 57717 表达 57717 следа 57716 ▁плана 57715 なし 57715 遣 57715 reide 57714 venida 57714 ïau 57714 ▁Ադրբեջանի 57714 ▁biał 57713 ▁목표 57713 essee 57712 vå 57712 wuk 57712 ▁ለማድረግ 57712 uncia 57711 ▁Vár 57711 ▁daarmee 57711 ▁asu 57710 ▁celkom 57710 ▁overzicht 57710 inginkan 57708 ρούμε 57707 ିଅ 57706 ▁ръка 57706 ഷേധ 57705 ▁ënn 57704 ▁לאתר 57704 时期 57704 ▁Uncategorized 57702 ▁bữa 57701 ovanjem 57700 cił 57699 ▁iww 57699 ▁phiếu 57699 ▁stage 57699 ▁анық 57699 middel 57698 ▁құру 57698 ▁ब्रेकिंग 57698 상품 57698 ԵՍ 57697 ၼ 57697 ▁Jennifer 57696 ▁bungtod 57696 ▁ማሳ 57696 ▁veien 57693 人都 57693 меш 57692 вяза 57691 ▁safari 57691 ФА 57690 ▁refug 57690 ▁ଦେଖା 57690 נאַ 57688 ▁تقل 57688 ДПМНЕ 57687 ▁selve 57687 状况 57687 валю 57686 ▁ندارند 57685 ▁számú 57684 aiche 57682 ▁hamm 57682 ensión 57681 ▁suic 57681 ▁бүлек 57681 espill 57680 ithatë 57679 ▁bakarrik 57679 ▁tijelo 57679 ilyn 57678 ▁cristian 57678 ▁ndar 57678 ▁tomon 57678 အတြင္း 57677 ▁subkategoria 57677 dw 57675 лькло 57675 ▁Међутим 57675 wako 57673 льню 57673 ദ്ധി 57673 सको 57672 ▁įmon 57672 ▁പെണ് 57672 ▁havet 57671 antunti 57670 ▁Христа 57670 ▁Niveau 57669 ▁posla 57668 ▁ಸಂಖ್ಯ 57668 ிருக்க 57667 ▁boje 57666 ▁সড়ক 57665 ▁Tiên 57664 ▁eier 57664 ▁Haag 57662 ▁Wei 57662 agara 57661 jalle 57661 гаарын 57661 ▁activiteiten 57661 ช่วยเหลือ 57660 ዋን 57660 ▁چان 57660 ▁லட்ச 57660 连续 57660 采访 57659 ▁иргэн 57657 ▁Mgr 57656 ▁находя 57655 絶対 57655 ▁vener 57654 ▁Vl 57653 ▁ministri 57653 ▁कोशिश 57653 ▁fogy 57650 ▁SEP 57649 ▁بهر 57649 ámica 57648 ▁ভূ 57647 ▁সম্ 57646 ▁anwo 57645 ▁vex 57645 ▁независимо 57643 ▁ලේකම් 57643 лцо 57642 ▁Abuja 57641 アニ 57641 ▁gözlən 57640 ▁ہو۔ 57640 ▁לתת 57639 ▁формата 57638 ▁financiar 57636 ▁хеҙмәттәш 57635 ▁Liza 57634 ▁факульте 57634 ▁единстве 57633 boa 57631 ויז 57631 ګې 57631 ▁σοβα 57631 ▁супру 57631 এর 57630 աքնն 57629 ovém 57628 ▁säll 57628 ▁بنسټ 57628 iraz 57627 ▁dihad 57627 ▁aizsardzības 57626 ▁conxuntu 57626 ປະຈໍາ 57625 ▁Ырымбур 57625 ▁القاهرة 57625 ▁Maps 57624 ▁tiešsa 57623 ▁frec 57622 ▁isit 57622 很难 57622 ▁fursa 57621 ▁Quiz 57619 ▁anjeunna 57619 ehdot 57617 ▁Аляксандр 57617 ▁ڏسڻ 57617 ▁Jé 57616 ссу 57615 に乗 57615 ٺا 57614 ▁العاده 57614 anek 57613 skis 57612 urios 57612 тком 57612 ▁còmhla 57612 ▁rabatt 57612 ▁лагер 57612 ▁ѕ 57612 ▁kiwari 57611 ▁көтө 57611 ▁השימוש 57611 udoj 57610 ।" 57610 ▁labarin 57610 ▁dvd 57609 ▁frü 57609 ▁ciento 57607 기의 57607 ▁ocasiones 57606 านคร 57605 ▁rana 57605 തിനെ 57604 ▁keamanan 57604 ▁ומת 57603 ▁ኤርትራ 57603 ▁پڑھیں 57602 emps 57601 ▁Одоо 57600 ahna 57599 ▁sertif 57598 podob 57597 gef 57596 ▁uqinisekise 57596 ▁виник 57596 ▁پيار 57595 ▁मं 57595 ▁უბრა 57595 opot 57594 əcəyi 57594 ▁green 57593 olana 57591 ▁(31) 57591 ▁Siti 57591 ▁relat 57591 რუ 57590 შენებ 57590 ▁مڪ 57590 ரல் 57589 ▁prisen 57589 quir 57588 ותה 57588 ▁apvien 57588 baovao 57587 ▁hors 57587 ▁məsuliyyət 57587 ▁сня 57587 ▁LLC 57586 engisa 57585 itaja 57584 ▁ზოგ 57584 ënten 57582 ▁paraq 57582 ▁kungsi 57581 ▁pequ 57581 ેરફ 57580 ▁објав 57580 ▁фаза 57580 راقي 57577 ▁공부 57577 richtung 57576 አት 57576 ▁դեպի 57575 тний 57574 ▁aikšt 57574 ▁പറയുന്ന 57574 ▁Sunn 57573 ▁ชั่วโมง 57573 цыяналь 57572 ▁لید 57572 əyin 57571 انهن 57571 ▁ด้าน 57571 batta 57570 ထဲက 57570 နယ် 57569 ▁novog 57569 ▁zatim 57569 웠 57569 활동 57569 際に 57568 ▁તું 57567 ochen 57565 लोड 57565 itsy 57563 iseni 57562 ▁Ου 57562 ▁පෙළ 57562 നീ 57561 ▁ዉ 57561 摇 57560 ▁automatisch 57559 karna 57558 ▁Sika 57558 ▁begynner 57557 произво 57556 ▁получили 57555 짝 57555 ikten 57554 ønner 57553 ໂນ 57553 úrt 57552 ▁gigant 57552 ▁поново 57552 ▁משל 57550 ▁djur 57549 өнө 57548 ▁Industrial 57548 ▁ochii 57547 ▁Nich 57546 arske 57545 在此 57543 etoje 57542 sob 57542 ▁کامیابی 57542 ▁perek 57541 ▁сярод 57540 ▁ટ્રે 57540 ▁ประจํา 57540 ▁arbeta 57539 ▁Đoàn 57538 渐 57537 чаў 57536 ▁агро 57536 ▁гроші 57536 ▁Marti 57534 有名 57534 猛 57534 ▁практично 57533 ▁බු 57533 ▁candidatura 57532 ▁hitan 57532 gob 57531 ▁находятся 57531 主动 57531 ▁ଯୁବକ 57530 ▁forbi 57529 быҙҙың 57528 ▁kolejne 57528 ▁prossimo 57527 ժմ 57526 ඳි 57526 ▁frukt 57525 unts 57524 ▁tuwin 57522 ▁ήθε 57522 ضيع 57519 ▁onih 57519 ▁жара 57519 หรัฐ 57518 ▁homose 57517 ▁συμφ 57517 ▁наче 57515 ▁કહી 57514 מבר 57513 ▁byddai 57513 ▁sente 57513 ▁verificar 57512 ▁чемпионатында 57512 ▁Jego 57511 ▁младе 57511 โฆษ 57510 ▁ողջ 57510 ▁ಮಾನ 57510 ▁Grie 57509 شتہ 57508 ▁Ադրբեջ 57508 роби 57507 ▁گیاه 57507 ▁ძლი 57507 เก็ต 57506 ▁माझा 57506 סא 57505 ಾಮಿ 57505 ▁коп 57505 ▁වීමට 57505 eces 57504 ologija 57504 éischt 57502 用の 57502 銀 57502 ▁náb 57501 ▁приложение 57500 ▁አሁንም 57500 śród 57498 ДВ 57498 שבון 57498 Hotel 57497 Max 57495 پان 57495 ▁directo 57495 ▁හමුදා 57493 ଲୋ 57492 ▁posve 57490 ▁दर्द 57490 ▁געהאט 57489 ▁പ്രവർ 57488 ▁හමුව 57488 最も 57487 adig 57485 ائلة 57485 ▁kabinet 57485 यद 57484 ▁kiny 57484 ▁мове 57484 मति 57483 ▁kloc 57483 ▁tæki 57483 ▁бәх 57483 ▁освіт 57483 ▁போன 57483 яб 57482 ▁Många 57482 ▁دقت 57482 ▁będziemy 57481 ▁tujuh 57481 ▁запита 57481 ▁ayı 57477 ▁hundi 57477 ▁tahansa 57477 ▁булма 57477 ▁здат 57477 ▁ҡулланыу 57477 ▁loạt 57476 ▁tysk 57476 ნას 57475 ▁veit 57475 ▁voord 57475 heids 57474 ondela 57474 шчэ 57474 ರ್ಜ 57474 ▁роли 57474 recek 57473 оръ 57472 ▁طبقه 57471 아서 57471 ▁Phường 57470 ▁پوهنتون 57470 ▁ať 57469 ▁nhắn 57469 ຕ້ອງການ 57468 ▁Tällä 57468 ေခၚ 57467 ▁дје 57467 γεται 57465 ▁SET 57464 ▁ofii 57463 ▁ႀက 57463 şu 57462 ▁condicións 57462 スマホ 57462 ▁eletr 57461 ▁първата 57461 ▁łatwo 57460 ▁odpowiedzi 57459 ▁பிறகு 57458 adooda 57456 נגד 57456 ▁Cód 57455 ▁mradi 57455 ▁സെക്ര 57455 RAC 57454 kości 57453 ndia 57452 ិនិត្យ 57452 Life 57451 ▁hym 57451 ▁kịp 57451 ▁سلامتی 57451 ▁강화 57451 ▁שיל 57450 ▁Gadaa 57449 ▁active 57449 ▁negocios 57449 laya 57448 woordig 57448 ▁ມື 57448 liklar 57447 ▁toimint 57447 弗 57447 ראַ 57446 ▁14.00 57443 ▁ASV 57443 ▁விப 57442 ▁pôvod 57441 ▁qend 57441 ▁συστή 57441 ▁നൽക 57441 ▁Ahoj 57440 ▁Fik 57440 ▁ovak 57440 koztató 57439 ▁ciekaw 57438 ▁iron 57438 ▁წარმ 57438 일보 57437 ▁sacer 57436 טיים 57435 ▁komplik 57435 λώς 57434 hani 57433 ▁piccola 57433 卓 57433 ट्स 57432 andaan 57431 ▁ඇවිත් 57429 ▁kwinguqulelo 57427 onkhe 57426 ೇಖ 57425 ▁ትልቅ 57424 ratie 57423 ▁посетите 57423 휘 57423 ▁qualcuno 57422 ▁አደጋ 57422 ▁አድርጎ 57422 рила 57420 وهای 57419 ▁toepassing 57419 ▁ക്ലാ 57418 zdem 57417 ሮችን 57417 ▁поэ 57416 ▁vyjad 57415 ▁ಚಾಲ 57415 ത്തിലുള്ള 57414 ▁पृष्ठ 57414 aruh 57413 เข้ม 57413 матра 57412 ▁mish 57412 ▁sutik 57412 rohet 57411 ▁كثيرا 57411 ▁chud 57409 巨大 57409 rates 57408 ▁filhos 57408 ▁tamamen 57408 ▁wyłącznie 57408 ▁zaczę 57408 ▁ограничен 57408 ▁ຕື່ມອີກ 57408 ▁ટો 57407 იმი 57406 ▁Andra 57406 lerc 57405 orme 57405 ▁aic 57405 שוט 57404 ▁μέλλον 57404 못 57404 ▁الاخت 57401 หลากหลาย 57399 ▁vote 57399 濃 57396 aliyeed 57395 ອາດ 57395 ▁LEG 57395 ▁კომპანია 57395 ▁estabil 57394 ▁јуна 57393 ▁سڃ 57393 ▁mədəniyyət 57392 ▁ඔහුට 57391 黒 57391 ▁ditet 57390 ▁better 57389 ▁פולי 57389 ▁amint 57388 ▁צילום 57388 ▁matéria 57385 гин 57384 ေရာင္ 57384 ▁Coop 57384 ▁pomocí 57384 ▁stranden 57384 ▁여러분 57384 ▁2.3 57383 ರಂಗ 57382 ▁electric 57382 ▁Rasmus 57381 ▁suunta 57381 ▁yerləşən 57381 쁜 57381 ▁toplum 57380 ▁biko 57379 нопіль 57378 ▁fóra 57378 ደረሰ 57377 ▁nähdä 57377 րաստ 57376 ▁rzeczyw 57376 ëff 57375 일까지 57375 카지노 57375 jament 57374 laatst 57373 ҙәттән 57370 डित 57370 ▁fjor 57370 ▁Galmudug 57368 ▁bip 57368 ▁podrán 57368 ▁предузе 57368 ▁શ્રે 57368 ▁үле 57367 HU 57366 ▁Torres 57366 ▁vefs 57363 álkod 57362 رار 57362 ▁случају 57362 ▁اولیه 57361 ▁dň 57360 からです 57360 的原因 57360 ▁jelentős 57359 ▁이는 57359 ▁gerecht 57357 ▁ಶೇ 57355 ▁នាង 57355 ▁Prince 57354 ▁kedudukan 57353 idlalo 57351 ▁കാരണ 57350 ؟“ 57349 ▁Jw 57349 ▁libben 57349 ▁landets 57348 plig 57345 ▁Presse 57345 ▁Workshop 57345 ▁cruz 57344 ▁حکام 57344 ▁വർഷ 57344 jež 57343 tał 57343 ▁intensi 57343 ▁markaz 57343 ▁төрлийн 57343 ▁Дмитрий 57342 询 57341 ikati 57339 ▁snar 57339 することは 57339 라운 57339 værre 57338 ▁Bossòst 57338 ▁dezelfde 57338 ▁deach 57337 ▁მიზ 57337 ▁Spider 57336 bör 57335 ▁bestyr 57334 ▁උත්ස 57334 ▁információk 57333 ▁lưng 57333 ▁αναπ 57333 věd 57332 ▁dyk 57332 ▁okuphelele 57332 ▁segundos 57332 ▁poslu 57331 ície 57330 ▁shekarar 57330 ▁sufer 57329 ጆች 57328 ▁Dass 57328 ▁tombol 57328 ýva 57327 を与 57327 ▁policia 57326 дев 57325 ▁barneh 57325 ▁deixou 57324 ▁fíu 57324 MAS 57323 ▁पोलि 57323 ▁plotë 57322 ▁બને 57322 ▁ആക്രമ 57322 ▁americana 57320 ▁solutions 57320 ▁असर 57319 ungsi 57318 દાન 57318 ▁засе 57318 ▁podr 57317 ▁지도 57317 ▁odstr 57316 ▁الصحي 57316 lė 57315 strument 57315 ▁받고 57315 ▁상담 57315 出会 57315 ▁Cultur 57314 ▁hải 57314 ▁vermel 57313 ▁äta 57313 必要があります 57313 記載 57313 ▁страници 57312 ▁fiu 57311 预计 57311 μαρχ 57310 ▁әдеби 57310 的朋友 57310 ▁financiare 57309 یسک 57308 ေဟ 57308 ▁Ohio 57308 ▁Misy 57307 ▁αγαπη 57306 ▁көше 57306 ▁السر 57306 agina 57305 verde 57305 ▁Náv 57305 ▁ໃນການ 57305 enlla 57304 ▁repor 57304 Aku 57303 ▁saņ 57303 ▁گاڑی 57303 ▁Armenia 57302 ▁технологий 57301 ไม่ใช่ 57300 ▁Letras 57300 стеза 57299 ▁Uhuru 57299 ▁২১ 57297 威尼斯 57297 เซ็น 57296 ▁τρι 57296 ▁BOK 57294 ▁risposta 57294 ▁рейтин 57294 schl 57293 ▁věci 57293 ▁المحت 57293 ቋም 57292 ▁gora 57292 ▁materiaal 57292 ▁veï 57292 ▁ಪರೀಕ್ಷ 57292 ▁oru 57290 Է 57288 ▁кап 57288 ▁хяна 57288 ▁לוח 57288 ▁provincial 57287 ▁Cenedlaethol 57285 łania 57284 gota 57283 urb 57282 ▁δουλειά 57282 ▁Mav 57281 ɗon 57280 γουν 57280 ▁ražo 57280 தன் 57279 ▁përsh 57279 ▁quelqu 57279 ▁uinne 57278 ▁meddwl 57276 ▁общество 57276 ▁አሉ 57276 ▁werom 57275 ▁gənc 57273 ▁welches 57273 ▁հատուկ 57273 စ်း 57272 endawo 57271 ▁सित 57271 😝 57271 техні 57270 ▁negatif 57270 альная 57268 ▁igång 57268 lacht 57267 وبات 57267 ▁итали 57267 作成 57265 西班牙 57265 ▁ចុង 57264 ▁តារ 57264 ▁مرڪ 57263 ▁Leer 57262 ▁Navarra 57262 ▁harbiy 57262 ▁고민 57262 랑스 57261 ▁nė 57260 ▁дерек 57259 出て 57257 कृष्ट 57256 ▁richt 57256 ▁پابندی 57256 ▁அனைத்து 57255 χό 57254 ▁općine 57254 ▁płat 57254 үрлі 57253 ▁sanoa 57253 ▁বু 57253 ▁оценка 57251 ▁ڪان 57251 ूरी 57250 ▁ඉතිහාස 57250 эрэг 57249 ▁befinden 57249 әхси 57248 ▁vaikams 57248 äß 57247 ▁bilir 57247 ▁ശരീ 57247 χών 57246 тивного 57246 itement 57245 ▁поту 57245 ਜ਼ਾਰ 57244 ▁rose 57242 cool 57241 istesch 57241 čenih 57241 കർ 57240 ▁असार 57240 矿 57239 Jak 57238 uktikan 57238 ▁raspuns 57238 ▁ọdún 57237 imassage 57235 ▁Kaart 57235 ጠኛ 57233 ▁hinne 57232 ▁értelm 57232 ▁İz 57232 wmni 57231 ▁Albannach 57231 phi 57230 ርሲቲ 57230 ▁دندان 57230 földi 57229 ▁сотру 57229 ▁Suriye 57228 ▁Bath 57227 ▁Hale 57227 ▁ថៃ 57227 قتص 57226 ▁dhaawac 57225 ▁یوازې 57225 פוי 57223 ▁himoya 57223 ▁sieki 57223 ▁להמ 57222 ▁булыу 57221 ▁କିଏ 57221 疼 57221 ▁دود 57220 ingal 57219 nologie 57218 Mir 57217 ▁Mission 57217 ▁ਜਨਮ 57217 ▁berikutnya 57212 ▁লাখ 57211 rettet 57210 ▁ísl 57210 ▁Ć 57210 ▁Экономи 57210 ▁चित्रपट 57210 ▁lecture 57209 ▁ڪيون 57209 imleri 57208 ▁helburu 57208 ▁আদাল 57208 менять 57207 60. 57206 ▁osztály 57206 ▁בּ 57206 ▁२९ 57205 หัวใจ 57204 ▁Rodr 57204 ▁mintha 57203 ▁uzdev 57202 ▁Visual 57201 ▁nzuri 57201 ▁comentar 57199 ▁शुभकामना 57199 irlar 57198 ضافة 57197 ▁tunay 57197 ▁fengið 57196 ▁Liel 57195 ▁оҙаҡ 57195 ▁Shirika 57194 ▁đỡ 57193 нні 57191 ▁მოსახლე 57191 ▁bər 57190 ▁pwoblèm 57189 ▁слика 57189 ▁ظهر 57189 ▁mennä 57187 παρ 57186 ▁lwen 57186 artulad 57185 ▁ରୂ 57185 tvrt 57184 仿 57184 ▁presne 57183 ▁गलत 57183 hæ 57182 ▁pelaksanaan 57182 ▁लामो 57182 icinn 57181 retim 57179 ▁ποσο 57179 ▁எனது 57179 ▁sae 57178 ▁Прода 57178 dakı 57177 tság 57177 ▁intención 57177 ▁যোগ 57176 ▁làrach 57174 ▁Сяр 57173 ▁одлука 57173 ▁راحت 57172 ▁ڪيترن 57171 വരുടെ 57170 ▁gaadh 57170 ▁основних 57170 ▁Ezen 57169 ▁ସହା 57169 ėdami 57168 ണാ 57168 ▁innde 57168 ennung 57167 ▁شدہ 57167 แส 57166 ▁Koch 57166 ▁Protest 57166 ▁cuvinte 57166 ▁deschide 57166 募集 57166 ▁gehéiert 57165 ▁verano 57164 ▁þín 57164 andaa 57163 ▁Todėl 57163 ▁ಅತಿ 57162 と感じ 57161 днаш 57159 処理 57159 ▁Laf 57156 绕 57156 ▁어린이 57155 ulasi 57154 ዕራ 57154 тябрь 57153 ▁latest 57152 zinha 57151 බැ 57151 ▁depl 57151 ▁általános 57150 usul 57149 ▁көңі 57148 ▁էի 57148 pár 57147 ▁algunes 57147 ▁aproxim 57147 ▁hoteli 57147 ▁גא 57147 ▁구조 57147 laydi 57145 ▁взаємо 57144 ▁sisa 57143 ▁slučaj 57142 emple 57141 ႐ွ 57140 立て 57140 ▁eliminar 57139 ńska 57138 ▁ເດັກ 57138 锋 57138 ▁Willie 57137 ▁међународ 57137 ▁신고 57137 χων 57136 ▁llargo 57136 ▁ubr 57136 ▁крају 57136 ▁unic 57135 בורג 57133 ▁БОЛ 57133 ▁dzisiaj 57132 继 57132 ▁slogan 57131 ▁ऊपर 57131 ▁Acesta 57130 ▁lêste 57130 ډه 57129 ▁ظهور 57129 ▁ಗ್ಯ 57129 ▁Sử 57128 Cor 57127 rasti 57127 ▁איזה 57127 நில 57126 ▁vorhand 57126 ▁சமூக 57126 ▁языка 57125 ▁учре 57124 ▁явно 57124 ęcie 57123 ण्याच्या 57123 ▁ezikoleni 57123 ▁Општина 57123 ędzy 57122 ▁ନିର୍ଦ୍ଦେଶ 57122 ▁optre 57121 Ста 57120 اسف 57120 ▁memen 57120 ▁товары 57120 坐在 57120 ubuhan 57118 ▁байланыс 57118 ▁55. 57117 ▁register 57117 είμενο 57116 ▁trebal 57116 RAM 57115 ▁Policy 57115 ▁awood 57113 鬆 57113 ▁ڏين 57112 פני 57111 ▁спеціалі 57110 ▁واکنش 57110 ▁एक् 57110 ▁devido 57109 ▁Zg 57108 ▁rolig 57108 ▁βίντεο 57108 ▁разнообра 57108 ጩ 57107 ▁soggiorno 57106 ▁ശബരിമല 57106 လက် 57104 ▁රැක 57104 ▁อิน 57104 ▁싶은 57104 ▁ስብሰባ 57103 凭 57103 adada 57101 kori 57100 17] 57099 ▁vyzer 57099 atasaray 57098 ▁beuki 57097 ▁nemet 57097 ▁ماج 57097 સાર 57095 ▁პროც 57095 serbi 57093 ▁кожен 57093 ▁լավագույն 57092 ▁زا 57092 ▁ସୁରକ୍ଷା 57090 nungen 57089 ▁fawr 57089 icznie 57088 łon 57088 흔 57088 ended 57087 konomi 57087 ▁jätte 57087 ▁tilt 57087 ▁имя 57087 샵 57087 ▁Оро 57086 interes 57085 raan 57085 ▁Tsara 57085 ▁කිසිම 57084 TL 57083 ▁tp 57083 ▁الکترونیک 57083 أل 57082 ▁diharapkan 57082 ▁fyw 57081 ▁metatra 57080 ▁տոկոս 57080 ▁czemu 57079 ▁ನೆನ 57079 фир 57078 ▁Проце 57078 ▁саве 57078 ▁oherwydd 57076 ေခ် 57075 ▁ném 57075 ▁ukupno 57075 ▁Amianan 57074 ▁béda 57072 ก็จะ 57071 ▁විජ 57071 доз 57070 ายุ 57070 ▁funt 57070 ▁Clin 57069 ▁Kommentar 57069 niausiai 57068 ғып 57068 自行 57068 izadas 57066 íser 57066 ▁ទីក្រុង 57066 ▁Daž 57065 ségi 57064 ▁zasi 57063 ▁رکھتے 57063 πτώσεις 57062 ▁Powi 57062 -80 57061 ▁kijel 57061 isema 57060 شيا 57060 ▁pinta 57060 ▁покры 57060 ▁Manu 57058 ▁says 57058 ▁pierwszej 57057 ▁დავი 57057 іден 57055 ekal 57054 ▁àṣẹ 57054 ▁되고 57054 ▁فیل 57053 likool 57052 ▁Gamb 57052 ▁Cosa 57051 ▁можност 57051 ▁पछिल्लो 57051 ம்ம 57050 ▁ਵਿਸ਼ 57050 jukan 57049 ẫ 57049 ▁jonkin 57047 ▁Bosnia 57046 ▁الجميع 57046 betal 57045 ร้าย 57045 ▁CONC 57045 ▁Lys 57045 ▁gern 57045 ▁միջոցառ 57043 ▁ਸ਼ਬਦ 57043 プレイ 57043 يتن 57042 ▁szenved 57042 ▁yoktur 57041 ▁თით 57041 omul 57040 ▁ناصر 57039 āru 57038 ▁Pomoc 57038 शनल 57037 สู้ 57037 바이 57037 ▁Congress 57036 ▁escorts 57036 ▁तस् 57036 довать 57035 ▁digun 57035 ▁zle 57035 ေငြ 57032 ▁ಬೆಳೆ 57032 ▁Bokonzi 57031 ▁жителей 57031 hjem 57030 ▁Seben 57030 ▁nueves 57030 არება 57029 ▁börn 57029 ▁හරියට 57029 חרות 57028 ▁Ռուսաստ 57028 ▁сег 57026 ▁20.00 57025 ▁ιδιαίτερα 57025 加拿大 57025 ▁saop 57023 ▁قاد 57022 ▁ypat 57021 音樂 57021 워크 57021 anging 57020 έβαια 57020 ▁intet 57020 ▁למעשה 57020 買取 57020 ▁сох 57019 ▁Mám 57018 ▁zapravo 57017 ▁نيز 57017 ระบุ 57016 틴 57016 ▁vetem 57015 ▁предела 57015 խմբագրել 57014 จี 57013 boko 57012 ▁occitans 57011 ▁versão 57011 ▁טיפ 57011 ▁لکيو 57011 ▁دقیق 57010 ▁hosting 57009 teria 57007 ▁ბათ 57007 খ্যা 57006 ▁Perusahaan 57006 мерно 57004 јт 57004 ▁céanna 57004 ▁pomoci 57004 белару 57003 ාවන් 57003 ▁correr 57003 ▁yiluphi 57000 dj 56999 ▁Filmer 56999 ▁часом 56999 ▁მიზე 56999 ▁Batman 56998 утбо 56997 ▁ಕರೆ 56997 poque 56996 ▁Uud 56996 ▁때문 56996 知らせ 56996 ▁pored 56995 ▁teken 56995 ंकर 56994 还在 56993 oub 56992 taxt 56990 ▁Terror 56990 ▁vertra 56990 ▁खेळ 56990 ▁Хүүх 56987 ▁טאָג 56987 ▁රණ 56987 ▁verkoop 56986 ▁όνομα 56985 ▁дайында 56985 щені 56981 ▁ίσως 56981 istab 56979 නෝ 56979 ▁ανάπτυξη 56979 umput 56978 ▁өгсөн 56978 ▁Buer 56976 ▁dhinac 56976 ▁visitas 56975 ઓમાં 56974 ▁náklad 56974 ждан 56973 ▁intelectual 56972 ▁ислам 56972 ▁Aufgaben 56971 ▁joga 56971 ▁kunskap 56971 ▁quería 56971 ▁سودا 56971 altern 56970 身體 56970 ▁....... 56969 ▁ખુબ 56969 룸 56968 ▁алко 56967 ▁протяжении 56967 ▁جایی 56967 ▁กําลัง 56967 ғол 56966 น้ําหนัก 56966 ▁54. 56966 ▁گستر 56964 ▁ମିଳିବ 56964 ▁프로젝트 56964 컵 56964 Sen 56963 terea 56963 ᄒᄒ 56963 ▁Groot 56963 ▁αγα 56962 灰 56959 मीन 56958 ▁myst 56958 ▁изготов 56958 ▁предприятий 56958 옷 56958 ▁երեւ 56957 ▁ausges 56955 ▁consta 56955 ▁sfâr 56954 ▁скар 56953 ▁праду 56952 ဘက္ 56951 ▁uzlab 56951 ▁অন্যান্য 56951 erait 56950 ▁regjer 56950 ▁Kuya 56949 ▁esimene 56949 ▁사고 56949 unami 56948 ▁technologi 56948 ▁Turun 56947 ▁ซึ่งเป็น 56946 μείου 56944 ▁úžas 56944 zao 56943 ▁epid 56943 肝 56943 ▁rrjet 56941 ▁sawijining 56941 lucht 56940 decznie 56939 куда 56939 торів 56939 ▁07, 56939 ▁camiño 56939 ▁practica 56939 kových 56938 utture 56938 ▁terbang 56938 ାନି 56937 ▁všichni 56936 ▁തിരുവനന്തപുരം 56936 čani 56935 ▁апре 56935 ▁স্থান 56935 ▁přírod 56934 ▁βό 56934 ▁ජාත් 56934 ▁организова 56933 ▁አምላክ 56933 schule 56932 ίδι 56932 ▁Бында 56932 ▁ẹrọ 56931 ▁betur 56930 ▁yoyote 56930 ▁слуха 56930 อาร 56929 ▁зареє 56929 身边 56927 arun 56926 ▁oddi 56926 aganda 56925 בען 56924 ▁Nielsen 56924 ▁notific 56924 віка 56923 เป็นอย่าง 56923 utuse 56922 Ін 56922 ▁ரஜ 56922 itāte 56921 ▁početkom 56921 ▁Hele 56920 ▁vurdering 56920 ມັດ 56919 ▁1997, 56919 ▁روپ 56916 ോദ 56915 ▁Expo 56914 ▁privata 56914 кем 56913 ▁domicili 56913 ▁tarkibiga 56913 ▁ғалим 56911 ▁mpango 56910 ▁квалі 56910 ▁बताउनुभयो 56910 ▁ହିଁ 56910 usna 56909 wegs 56909 มาณ 56909 ▁گران 56909 ▁enll 56907 ▁የቅ 56907 ქართ 56906 ▁закљу 56906 ▁természetes 56905 ▁избо 56905 ▁тән 56905 ▁амаль 56904 TAA 56903 әж 56903 ▁ukhetha 56903 ▁המלון 56903 ▁கண்டு 56903 ▁leverand 56902 ▁terkena 56902 лајн 56901 およ 56901 ▁llawn 56899 idek 56898 SQL 56897 öör 56897 ทับ 56897 的話 56897 ที่ใช้ 56896 ▁persoonlijke 56895 ▁орден 56895 ศาส 56894 ናቸው 56894 ▁עב 56894 向け 56892 ▁repert 56891 오피 56891 ▁दर्श 56890 ▁Glass 56889 ▁jirin 56889 ▁regte 56889 achdan 56888 ▁duerno 56888 ▁پلیس 56887 ▁ಆನ್ 56887 ▁ליד 56885 ▁표시 56885 ▁xanım 56884 ▁ଚିକିତ୍ 56884 ▁praia 56883 ▁hannu 56881 ACIÓ 56880 ▁especialment 56880 ▁لاڙ 56880 ▁bölges 56879 ▁принципи 56879 产品的 56879 ▁журнал 56878 ваныя 56877 ▁האחרון 56877 Cap 56876 ▁Prozess 56876 ▁алуға 56876 րագր 56875 ቂት 56874 ▁Eso 56874 expression 56873 vika 56873 ▁podrás 56872 შიც 56871 দুল 56868 ▁róla 56868 ▁পরিচাল 56868 soa 56866 έντ 56865 צוני 56865 ເບິ່ງ 56865 ▁góc 56865 nania 56864 ▁Müsl 56864 ▁innego 56864 ▁ઘર 56864 цан 56863 ▁Gag 56863 ▁shen 56863 ▁ലക്ഷം 56862 ▁ອີ 56862 ▁incluyendo 56861 案内 56861 xs 56859 ാസ്ത്ര 56859 ▁etali 56859 ▁patog 56859 ▁වැඩේ 56858 ▁դես 56855 tallet 56854 ▁கண்ட 56854 หาด 56852 Ан 56851 ▁appara 56851 ▁نح 56850 郵 56850 ▁உரி 56848 ▁eziph 56847 ▁püh 56847 ▁xogo 56847 ▁گست 56847 ▁setor 56846 ▁ιδιω 56846 ▁გეგ 56846 ▁ໃຫມ່ 56845 ▁aangeb 56844 ▁bhfeidhm 56844 ▁різні 56844 ▁دوي 56844 inyo 56842 ແນກ 56842 ▁mètres 56842 ▁አፈ 56841 ENTA 56839 ଦନ 56839 ▁erfolgt 56839 ▁îți 56839 ▁карту 56839 шева 56838 ▁Nghị 56838 ▁பிரப 56838 ▁betala 56837 ▁hoid 56837 ▁schoon 56837 ▁πρόεδρος 56837 ▁யோ 56837 ▁SSD 56836 ത്വം 56835 ▁ಒಪ್ಪ 56835 さを 56835 ▁2012- 56834 ▁Sonder 56834 ▁kompiuter 56834 ▁tõttu 56834 ونت 56833 ▁Shkod 56833 ▁schrift 56833 ▁प्रभावित 56832 ▁გახდა 56832 ether 56831 ▁Ама 56829 ▁визу 56829 ▁Վարդ 56829 ▁nguyện 56828 ▁месе 56828 ▁осор 56828 preek 56827 מייל 56827 ቡነ 56827 ▁Henero 56825 ▁pakan 56825 ▁nase 56823 ▁اکث 56823 ▁ಬೇಡ 56823 ▁indenfor 56822 Dec 56821 Alpes 56820 ▁nokuvik 56819 ▁Guardian 56818 ▁sugger 56818 ▁Bununla 56817 대회 56817 isaki 56815 ▁Seat 56814 ▁houdt 56814 ▁keram 56814 ▁trăm 56814 ▁Zul 56812 ▁liit 56812 ▁poliisi 56812 ▁beleid 56811 ▁ಚಿತ್ರದ 56810 mane 56809 තක් 56809 වම 56809 ▁съюз 56809 ôme 56808 ▁ríomh 56808 ▁କଲା 56807 ▁COMP 56806 ▁Undang 56806 ▁kalbos 56806 ▁povrch 56806 ▁сабе 56805 ansan 56804 tahun 56804 ▁dür 56804 ▁स्वयं 56803 ▁miesią 56802 ▁возрасте 56802 ikte 56800 صیلی 56800 ▁Jup 56800 ▁шығару 56800 ▁ngalak 56799 мпи 56798 ▁(195 56797 ▁bárm 56797 ▁कोणत्या 56797 が高 56796 artet 56794 seur 56794 ▁fx 56794 ▁susitik 56794 ▁доаѓа 56794 세계 56794 SKE 56793 ▁leje 56793 യ്യ 56792 ▁Org 56791 adua 56790 ▁ತುಂಬಾ 56790 tuurka 56789 ▁figure 56789 ▁propone 56788 ▁раҫ 56788 ▁шалт 56788 ▁એના 56788 ▁Λε 56787 ▁zatem 56786 ▁तरु 56785 ▁Kell 56784 ▁kve 56784 ▁namni 56784 ▁الصور 56784 ຊື່ 56783 ▁vodo 56783 ▁သံ 56781 ▁restitu 56780 ▁salaam 56780 閱 56780 ▁науково 56779 ▁चुकी 56779 ▁භාවිතා 56779 ▁fiets 56778 ▁fizet 56778 ▁προγρά 56778 ▁Fier 56776 ▁-. 56775 ▁ಧನ್ಯವಾದಗಳು 56775 न्व 56774 ดิ์ 56774 ▁Verkehr 56774 ▁રહ્યું 56774 188 56773 धिकारी 56773 ម្ង 56773 ▁κέντρο 56773 ▁sluit 56772 사지 56772 ▁factores 56771 ▁indo 56771 လႊာ 56770 ▁kuliko 56770 ▁sanoo 56770 esas 56768 ▁آقا 56768 فقت 56767 ▁នយោបាយ 56767 ▁mediteranyo 56766 äten 56765 ▁tehnyt 56765 ▁פארו 56765 ▁Cầu 56763 ▁surpr 56763 ▁arfer 56762 ▁medewerkers 56761 ▁आशा 56761 ▁прой 56760 ഹമ്മ 56759 ▁erla 56759 ▁njihovih 56759 ▁Eden 56758 ശംസ 56757 ▁abych 56757 ▁базо 56757 ▁тов 56755 ▁בלויז 56755 ລູກ 56754 ▁የውጭ 56754 iongoni 56753 ▁tuta 56753 hrom 56751 ▁nsi 56751 ერხ 56750 ▁জুলাই 56750 તન 56749 ▁humna 56749 ▁søge 56748 ▁ваҡиға 56748 USU 56747 ▁വർ 56747 ▁всеми 56746 呵 56744 ▁dostav 56743 atsz 56742 гуля 56742 ભાગ 56742 ▁colección 56742 ▁تایید 56742 ▁Skin 56740 ▁Szám 56740 ▁созна 56740 ثة 56739 ලියන 56739 ▁toteut 56739 ▁τιμή 56739 ন্টার 56738 학과 56738 ▁юни 56736 ວຽກງານ 56735 ▁Wyd 56734 ▁pomys 56734 ladda 56733 ▁mongwe 56733 驻 56733 ▁Kebangsaan 56732 ▁mengubah 56732 ▁मां 56732 ▁চিকিৎস 56732 ຮອງ 56731 ▁Isofthiwe 56731 ▁અનુભ 56731 ტკიც 56730 ▁ଡାକ୍ତର 56730 াপতি 56729 ▁۱۳۹۵ 56728 ▁크게 56728 ங்கில 56727 ▁ಧರ್ಮ 56727 ▁lõpp 56726 gati 56725 ▁महाराष्ट्रा 56725 歴史 56724 ▁cilik 56723 ▁öyrən 56722 กรณี 56721 ▁poroz 56721 ▁rekor 56721 来源 56719 ▁comum 56717 ▁татва 56717 ▁өгч 56717 лених 56716 ▁začala 56716 ▁ਅੰਤ 56716 klassesch 56715 ▁taz 56715 ▁같습니다 56715 ▁فیصد 56713 ▁बच्चा 56713 AREN 56711 ▁nekad 56711 ▁социальной 56711 ▁tveg 56709 ▁xusti 56708 ▁አበ 56708 ijed 56707 ▁bertu 56707 ▁valori 56707 ▁מיליון 56707 ▁medžiag 56704 ▁Lui 56703 ▁éner 56703 ciación 56702 ācijā 56702 ▁anket 56702 ▁babagan 56702 ▁ushtr 56702 ▁जिसमें 56702 ▁ծան 56701 ▁ବିଶେଷ 56701 髪 56700 ▁bereg 56698 ▁marrëdhë 56698 ▁따른 56697 ▁پلار 56696 ▁ប្រាក់ 56696 ▁naved 56695 ▁unsa 56694 ▁तपाईको 56694 ▁doskon 56693 ifiso 56692 ուպ 56692 ▁ຕີ 56692 ▁rahvusvah 56691 ▁گول 56691 ിക്കുകയും 56688 ▁gấp 56688 मार्ग 56687 合法 56687 ▁อย่า 56685 ▁baxa 56684 ▁תוכלו 56684 ▁ევროპ 56684 ukke 56683 tici 56682 ▁Ferrol 56681 ▁جعل 56681 ▁parf 56680 дку 56679 ▁rendkív 56679 ▁یمن 56679 ▁दाहाल 56679 јима 56677 ▁keladi 56677 prime 56676 відом 56676 ▁тести 56676 ▁umas 56675 ▁Хәй 56675 ▁højt 56674 presenta 56672 ଲୁ 56672 ▁نموده 56672 ້ຽມຢາມ 56671 ▁Wich 56671 isteddfod 56670 άσεις 56669 тиву 56669 ▁Александра 56668 ▁வீர 56668 awu 56667 ▁تعرض 56667 给予 56667 ologii 56666 ▁dün 56666 ▁kolekt 56666 ▁obblig 56666 ▁бәйләнеш 56666 ▁repara 56665 alubongan 56663 ysteem 56661 ▁déanta 56661 ▁সো 56661 ▁yhteyttä 56660 ▁የኤ 56659 ِّ 56657 ▁resen 56657 ▁schwier 56657 রুল 56656 ▁чэр 56656 езда 56655 ▁Alpha 56655 ▁plod 56655 ▁жолы 56655 ▁començament 56654 ▁ruke 56654 ▁ажы 56654 Gesetz 56652 atanya 56651 aveta 56650 排名 56650 opu 56649 ▁sees 56649 ▁ЗО 56649 ▁приложения 56649 ▁брен 56648 ▁ਚੰਡੀਗੜ੍ਹ 56648 닥 56648 ovatel 56647 ▁Entscheidung 56647 ▁Önkormány 56647 ▁চার 56647 פֿט 56646 ▁କମ୍ପ 56646 lha 56645 ▁нийг 56645 qust 56644 ▁עפ 56644 ବାଇ 56642 ▁Tipps 56642 ▁twór 56642 Pal 56641 ▁fenó 56641 ▁पसंद 56641 ▁Retro 56640 ▁felad 56640 ▁vemos 56640 refter 56638 ▁бөтөн 56638 ▁sisältää 56637 τρί 56635 ာင့္ 56635 ▁kakovost 56634 ▁σχεδόν 56634 ▁турнир 56634 ▁pilo 56632 ▁többi 56631 ▁мр 56631 ▁وسیله 56629 ▁pubbl 56628 ▁ਦੁਆਰਾ 56628 ▁ନୀ 56628 ▁კონკ 56628 aktır 56627 ▁frij 56627 ราชการ 56626 ▁Tp 56625 ▁jaga 56625 ҡыр 56622 ▁Ane 56622 ▁hisoblanadi 56622 ▁vremen 56622 ▁cục 56621 ▁ostalih 56621 ▁माया 56621 voriť 56620 holi 56619 legum 56619 žuje 56619 ▁procesa 56619 espa 56618 рави 56618 ▁pedagóg 56618 ▁блю 56618 ▁ተናግ 56618 んと 56618 ാതി 56617 ▁kaliteli 56617 ▁دوام 56617 ▁ขณะ 56616 70. 56614 ▁minuts 56614 roje 56613 ▁næsta 56613 ानो 56612 ▁egoera 56611 ▁ਬੰ 56611 agenda 56610 ▁Түбән 56610 ▁ظل 56610 ▁upotre 56609 ▁1、 56608 ▁múlt 56608 maskin 56607 孔 56607 ତ୍ତ 56606 ▁Milf 56606 ▁Natuur 56606 ▁Zil 56606 ▁geword 56606 arbejder 56605 ▁FREE 56605 čnu 56604 ▁Keny 56603 ▁OCH 56603 ▁ਸਮਾ 56603 utch 56601 ▁nettdating 56600 ▁پشتیبانی 56600 čica 56599 ▁مبلغ 56599 ▁Soma 56597 ▁чемпі 56597 共享 56597 ▁njimi 56594 ▁juur 56593 ▁modèle 56593 iyak 56592 வேண்டும் 56591 ▁meskipun 56591 ▁ଅମ 56591 ▁Vora 56590 ▁keba 56590 ▁toca 56590 ▁medium 56589 ▁ДУ 56589 いです 56589 Cat 56588 ?). 56587 ადგენ 56586 ▁Mới 56586 ▁demander 56586 ▁øjeblik 56586 ithre 56585 ▁Shacabka 56585 ▁livs 56585 ▁בשע 56584 措 56584 nilai 56583 ▁ຊາວ 56583 至今 56582 ▁kaupunki 56581 ▁poskr 56581 ▁ábhar 56581 ▁czeka 56580 ▁ಮುಖ್ಯಮಂತ್ರಿ 56580 ▁መመ 56580 ▁শু 56579 ▁zapoz 56578 셔서 56578 esked 56577 ▁okna 56577 ficia 56576 상의 56573 ▁ہوج 56572 ไร้ 56571 ▁Mūsų 56571 ▁kuanzia 56570 ▁மதி 56570 ിലീ 56569 ▁Ramadhan 56569 ▁অভিয 56568 이란 56568 ▁Patienten 56567 ▁નોંધ 56567 ریح 56566 ▁umumiy 56566 ▁zavod 56566 ▁رست 56566 ▁ಮದುವ 56566 anki 56565 ▁Tijdens 56565 ▁tuom 56563 ▁بازگشت 56563 距 56563 ե՞ 56561 ▁desider 56560 конферен 56559 ▁aluminium 56559 ▁noda 56559 ▁Скуп 56559 ▁ಅರ್ಥ 56559 ▁මොකක් 56559 ichert 56558 ▁Αφ 56558 ▁ທຣໍາ 56558 وبار 56557 ▁dedicar 56557 ▁huske 56557 ▁infrastrukt 56557 eluarkan 56556 यता 56556 ▁יב 56556 باء 56555 ຈໍານວນ 56554 ▁повинен 56554 χοι 56553 ▁kotiin 56552 ▁айырма 56552 ▁сакавіка 56551 ▁ବିକ 56551 ▁сообщения 56550 ▁ಸಂಪರ್ಕ 56550 edingt 56549 ▁kotak 56549 ▁ryzy 56549 ▁ора 56549 लेस 56548 ▁Danes 56548 ▁رپورٽ 56548 opha 56547 न्दा 56547 чера 56546 ▁тады 56546 ▁masuul 56544 ▁এশ 56544 ▁ግለ 56544 ন্ত্রণ 56543 ▁amics 56543 ităţi 56542 ändert 56542 ▁prostredníctvom 56542 ▁Display 56541 ▁episod 56541 ▁хүүхдийн 56540 ▁հյուր 56540 ੨ 56539 ዛዝ 56538 ▁ब्य 56538 ▁Tên 56537 ▁kret 56537 的前 56537 ezeigt 56536 ▁karbon 56534 ▁rii 56534 ▁wybrać 56534 ▁zupełnie 56534 ▁현실 56534 ▁Ident 56533 ੋਹ 56532 ▁реал 56531 gea 56530 ▁смет 56530 ▁141 56529 ▁snakker 56529 ဘီ 56528 ▁XML 56528 ▁Bold 56527 ▁Samtidig 56527 ▁kuriame 56527 ▁обикнове 56527 ▁וואו 56527 ▁फुटबल 56527 ocar 56526 ▁බලාපොර 56526 ▁konular 56525 ▁vagina 56524 ▁розташо 56524 ସମ୍ 56523 zł 56522 דין 56522 ▁pyn 56522 בתי 56521 ▁cymryd 56521 ▁плюс 56521 ▁پوه 56521 ▁pārliec 56520 ▁Thứ 56519 ▁Usaha 56518 ▁berhubungan 56518 ▁durung 56518 tuig 56517 ▁sauran 56517 ▁Rí 56515 ▁enfrent 56515 ਟਿਆ 56514 ▁boll 56514 ▁Віцеб 56514 مرہ 56513 ▁মঙ্গলবার 56513 inach 56511 ▁Ella 56511 ▁પુસ્ત 56511 ▁wichtige 56510 ▁Nikola 56509 ▁dalj 56509 ▁eskol 56509 ▁takmičara 56509 әндәр 56508 érium 56506 ак 56506 ▁сму 56506 ುವುದಿಲ್ಲ 56505 ลิ๊ก 56505 ကူး 56505 ▁Brook 56505 ▁гурван 56505 ▁bertanggung 56504 فوذ 56503 ्नुहोस् 56503 ▁individuell 56502 ▁ಸಮಾಜ 56502 ▁төрөл 56501 ▁איינ 56500 éseket 56499 ▁љубов 56499 ▁kooban 56498 提示 56498 歷 56498 ▁رم 56497 ▁ପୁରୁ 56497 ológico 56494 ▁fines 56494 ▁מהר 56494 urende 56493 ▁chào 56492 ▁okresie 56492 ისმ 56489 ▁ישרא 56489 ▁ತುಂಬ 56489 gla 56488 ssen 56488 ▁mācību 56487 ustin 56486 hañ 56485 ▁kezel 56485 ▁समिती 56484 ahim 56483 ворю 56483 ▁piedāv 56483 Het 56482 ▁Giao 56482 してる 56482 ukkig 56481 ▁ರಂದು 56481 ▁menghad 56480 huwalense 56479 ▁ଏମିତି 56479 akš 56478 സഭ 56478 ▁ď 56478 նենք 56477 trer 56476 კომ 56476 cą 56475 ilé 56475 ▁Our 56475 ปัญ 56474 ▁liggen 56474 ▁инве 56474 ஞர் 56473 ▁Duh 56473 ▁автобус 56473 ▁hinatag 56472 50) 56471 कलन 56471 ▁שאלה 56471 압 56471 ▁ọch 56470 ▁numbers 56468 ▁навчальних 56468 ▁อี 56468 ▁cint 56467 ▁ਸਹਿ 56467 ▁Мінск 56466 ▁ملڪن 56466 AKAN 56464 ्याने 56464 孩子的 56463 ▁новем 56462 radd 56461 ▁brott 56461 ▁Ξε 56461 ▁БО 56461 ▁превра 56460 ▁Realiz 56459 ▁būdu 56459 ▁Liq 56458 ▁Ubuntu 56458 End 56457 ▁yilning 56457 ▁രാത്രി 56457 usok 56456 ▁Pievien 56456 ▁åren 56456 ▁کلي 56456 ข่าย 56455 ▁ungdoms 56455 ▁halinde 56454 ▁kūry 56454 gyű 56452 ▁baxım 56452 ▁шаардлага 56452 κατά 56451 ବ୍ୟ 56451 ▁usi 56450 үүлэн 56449 ▁Böyle 56449 ▁unver 56449 udag 56448 imte 56445 kerung 56445 ▁quotidien 56445 ▁rating 56445 ▁النساء 56445 gbar 56444 ▁nedenfor 56444 ▁따라서 56444 อาจจะ 56443 ▁Սակայն 56443 ▁Dash 56442 ▁khắp 56442 ▁असोज 56442 ▁දෙදෙන 56442 ▁shpirt 56441 ▁zcela 56441 ▁Apan 56440 ▁anjara 56440 ▁attē 56440 ▁prioritet 56440 ▁середи 56437 כלה 56436 ▁Irlanda 56435 ▁zapro 56435 ▁jesteś 56434 ▁Helen 56433 ilda 56432 ▁Boeing 56432 ljed 56431 ▁hề 56431 ▁ahhoz 56430 ▁новий 56430 પ્ટ 56429 เม็ด 56429 ▁fördel 56429 ▁ವ್ಯಕ್ತಿ 56428 塗 56428 ▁Haala 56427 აკეთ 56426 ▁praktisk 56426 ingų 56425 ▁jawaban 56425 ▁məşq 56425 Nag 56424 στών 56424 ▁fiche 56424 ▁عربی 56424 ▁तेज 56424 ▁acti 56423 鳥 56423 andaj 56422 րե 56422 sége 56421 шою 56421 ▁KG 56421 ▁наприклад 56419 upid 56418 ▁Etiketler 56418 ▁ашық 56418 στευ 56417 ▁ایجن 56417 ▁ado 56416 ▁σύνο 56416 ▁Дом 56416 εύθυνση 56415 ടിക്ക 56415 ▁bilmir 56415 ▁otomatis 56415 ▁шука 56415 负责人 56415 uwen 56414 ▁انکار 56414 ▁معدن 56414 ที่น่าสนใจ 56413 ▁engas 56413 包含 56413 ▁felelő 56412 ▁глас 56412 ▁Consellería 56410 ▁دھما 56410 ▁ഒറ്റ 56410 besch 56409 estros 56409 ▁մամ 56409 ступу 56407 ▁Пасля 56407 ▁ehit 56406 बाब 56404 ଭେ 56404 ▁도움이 56404 ▁dhaliny 56403 ▁ín 56403 ▁artyst 56401 ▁ortiq 56401 ▁mùi 56400 ▁zabav 56400 isge 56399 śmie 56398 ファン 56397 ुनिक 56395 มูล 56395 ▁automatisk 56395 აზღვ 56393 ▁olmad 56393 ေႏြး 56391 ▁zouden 56390 ▁бірнеше 56390 اتھ 56389 adiť 56388 ▁рынок 56388 inājum 56386 ဆံု 56385 ▁имао 56385 ▁тооцо 56385 र्ता 56384 ▁Bayi 56384 ▁navnet 56384 argin 56382 vätt 56382 အတွက် 56382 ▁वर्षको 56381 скими 56380 ข้อความ 56380 ñeiro 56377 اتب 56377 ▁Univerz 56377 ბურ 56376 ▁לקוחות 56376 Holl 56374 ▁bigarren 56374 ▁ажи 56374 ▁неабходна 56372 ▁რომელი 56372 ▁인정 56372 ▁тобто 56371 ੰਪ 56369 ▁vlád 56369 noma 56368 ▁Verbindung 56368 ▁गुजरात 56368 άλυ 56367 міра 56366 ▁hiki 56366 ▁încât 56366 ▁Republi 56364 ▁αφο 56364 人民币 56364 angon 56363 bida 56363 ಿಯೇ 56363 късно 56362 ▁alten 56362 ▁Львові 56362 ▁внимания 56362 ▁ສອງ 56362 ▁utstyr 56360 飲み 56360 সল 56359 ўных 56357 ▁utveck 56357 íció 56356 ▁dold 56356 ▁терористи 56356 ▁اليڪشن 56356 ▁கலந்து 56356 არგებ 56355 ▁efektiv 56352 ▁rica 56352 ▁эшләгән 56352 ραπε 56350 ▁nyang 56350 번째 56349 ▁එල 56348 ▁되었다 56348 炼 56348 ක්ති 56347 róż 56346 ложени 56346 ▁alıyor 56346 ▁بالای 56346 ▁عادت 56346 ▓ 56346 腳 56346 ▁работода 56345 ▁بہترین 56345 ▁ସଚ 56345 ▁ඔව් 56345 ▁ammatt 56344 малары 56343 ▁ilość 56342 ▁taca 56342 delingen 56341 ▁факта 56340 市场的 56340 鉄 56340 раас 56339 ▁Process 56338 ▁haavon 56338 ▁לעצט 56338 ▁الطعام 56338 ▁instalación 56337 ▁sokongan 56337 சே 56336 má 56335 zgl 56335 ▁disi 56335 ▁Әле 56335 ▁Freitag 56334 ▁निर्देशन 56334 เสร็จ 56333 ▁verschiddene 56333 ▁sanga 56332 ▁вещества 56332 leman 56330 сега 56330 ▁eigent 56330 ▁ಅಂಗ 56330 стояние 56329 ▁Нүүр 56329 どのような 56329 ամարդ 56328 ຄືນ 56328 ன்க 56327 ▁نوموړي 56327 涵 56326 køb 56325 ոպ 56324 ▁আন্তর্জাতিক 56324 ▁gluten 56321 ▁неговиот 56321 ▁וואלט 56321 htm 56318 ▁ઘણા 56318 ▁Soy 56317 ▁לאור 56316 ▁klasy 56315 šaj 56314 ▁DER 56314 ▁nikada 56314 ▁ынты 56314 ▁dobila 56313 kého 56312 ћег 56312 ▁acele 56312 inaren 56309 50% 56308 сува 56308 ເທັກໂນໂລຈີ 56308 ▁feltét 56308 ▁výbor 56308 ▁בלאט 56308 ▁درک 56307 不可能 56307 ▁γύρω 56306 ▁مسکن 56306 ಕೊಳ್ಳ 56305 ობენ 56305 bhadh 56304 ▁darri 56304 ▁Бож 56304 сбук 56303 ▁kahden 56301 ▁කවු 56301 ▁fê 56300 ▁Тиймээс 56300 centrum 56299 وغ 56299 ▁உண்மை 56298 ▁Yên 56295 agric 56294 inikeza 56293 ▁organiséiert 56293 ▁Aalborg 56292 大多数 56290 ющем 56289 ▁टेल 56289 zionali 56288 ▁mott 56288 ▁tokia 56288 ▁بالك 56288 iktet 56287 ▁Каб 56287 ▁такава 56287 ▁گزار 56287 ▁nef 56286 ุล 56285 ▁lipsa 56285 ▁región 56285 ▁tájé 56285 cripció 56284 ▁gamit 56284 ▁projekty 56284 ົງການ 56283 ▁خالی 56283 릿 56282 bry 56281 quket 56280 ർച്ച 56280 ▁گزینه 56280 ▁müzak 56278 ▁Islom 56277 ▁oraș 56277 生活中 56277 aksa 56276 йшоў 56275 ▁දූ 56275 ▁right 56274 тев 56273 ▁pakar 56272 ▁تذ 56271 ▁kunj 56270 ▁zahtjev 56270 athan 56269 eceğini 56269 ▁dhulka 56269 שטעלן 56267 haven 56266 ▁байдлын 56266 ▁шка 56266 ▁просмо 56265 ▁የሰው 56263 никалық 56262 ▁abah 56262 elaa 56261 isille 56261 όνι 56261 ▁ҡатнашты 56261 ▁અંત 56261 ▁තියෙන්නෙ 56260 дама 56258 ▁omus 56258 ீடு 56257 ▁tirsdag 56257 ▁επίπεδο 56257 ▁золо 56257 ▁приобрести 56254 ▁elej 56253 ▁sonucu 56253 ▁ücretsiz 56253 ▁болест 56253 ▁الري 56253 ▁Konse 56252 ြာ 56251 ់ 56250 inois 56249 ▁BAH 56249 ▁iedzīvot 56248 ▁dröm 56247 ▁potencia 56247 utanen 56246 ▁tájékoztat 56246 ▁ପ୍ରାୟ 56246 ▁പ്പെ 56246 ▁ვიცი 56246 ▁Bik 56245 ▁ანგარი 56245 baliw 56244 ▁Ditaba 56244 ▁Hrad 56244 ▁įvyk 56244 ▁მცირ 56243 ▁Potom 56242 ▁今回は 56242 ulara 56240 ▁Transpar 56240 ▁recoñecemento 56240 ▁Vergleich 56239 ▁dijieun 56239 텍 56239 төн 56238 რია 56238 mód 56236 ▁Sunday 56236 ▁ever 56236 ▁concellos 56235 ▁dhaqaalaha 56235 ▁humo 56235 ▁smu 56235 usteko 56233 ▁женщи 56233 逐渐 56232 ▁informazio 56229 ▁काळ 56229 untain 56228 ▁kërkes 56228 াক্ষ 56227 ▁rodziny 56227 adın 56226 ▁doga 56225 ▁feedback 56225 ▁busnes 56224 ▁gyógy 56223 ▁שנות 56223 ▁zatiaľ 56222 dachadh 56221 ▁έσ 56219 стеме 56218 ย้าย 56217 ▁muyeres 56217 ▁זייַן 56217 ▁ಪ್ರವಾಸ 56217 डो 56216 ▁nấu 56216 ▁datele 56215 anaidh 56213 ▁grö 56212 ▁lembut 56212 ▁Οκτωβρίου 56211 όδου 56210 ▁роля 56210 företag 56209 धु 56209 චාර්ය 56209 ▁NAG 56207 ▁galven 56207 ▁መሠረት 56207 ▁niente 56206 ▁vehículos 56206 ▁تبص 56206 篮 56206 ▁bing 56205 ▁өҙ 56205 поте 56202 ಢ 56202 ▁eki 56202 เมตร 56201 päivä 56200 ▁pind 56200 ▁চৌ 56199 っていく 56199 ▁nja 56198 ▁태그의 56198 ▁summa 56196 ▁общего 56196 lund 56195 ▁ያልተ 56195 ▁Siis 56194 ▁علماء 56194 emir 56193 ล่า 56193 toc 56192 ▁гръ 56192 ımızda 56191 ▁Pent 56191 枪 56191 ▁arís 56190 ▁LR 56189 onnement 56188 ▁uzl 56188 ญาต 56187 ưỡng 56186 СТО 56186 ▁peran 56186 суд 56185 билей 56184 ▁CONS 56184 ▁Gez 56184 ▁jälleen 56184 etrie 56182 ষ্ঠ 56182 ▁ಪಟ್ಟ 56181 ាជិក 56180 diem 56179 ▁Zou 56179 ▁Экс 56179 ▁බැහැ 56179 ▁နာရီ 56177 ▁village 56174 ▁ചെറു 56174 ▁Meán 56173 ▁stamp 56173 ▁දැක්ක 56173 ណ្យ 56172 ▁emerg 56172 ▁haluaa 56172 ▁recone 56172 ▁Arusha 56171 infl 56170 តីត 56169 ▁Webseite 56169 ▁fotografii 56169 ▁dauk 56168 ▁kontinu 56168 ▁epiz 56167 ▁wilde 56167 一點 56167 ▁múltiples 56166 ▁ফির 56166 ▁เริ่ม 56166 ▁Uusi 56165 ▁आपने 56165 rẹ 56164 டாது 56164 ▁histórica 56164 ▁വാഹ 56163 演出 56163 ▁Libre 56162 ▁Kennt 56157 ▁nā 56157 ▁हासिल 56157 对此 56157 ▁αθλη 56156 ▁bevind 56155 ▁potrà 56155 ▁51. 56154 ▁paix 56153 pored 56152 tuak 56151 ထမ 56151 ▁xon 56149 ▁پرسش 56148 ▁უსა 56148 _( 56147 သု 56147 ▁Рай 56147 arið 56146 ▁głównie 56146 ▁majandus 56146 ▁сітуа 56146 ίσουν 56145 ▁durée 56145 ▁raus 56145 ▁عثمان 56145 ▁ਲਿਖ 56145 ▁tafel 56143 ▁technoló 56142 ▁ұсыны 56142 ▁svib 56141 ▁ακριβώς 56141 ▁marami 56140 ▁เลย 56139 ซี่ 56135 fanin 56134 ច្ឆ 56133 دیگر 56132 ထားတဲ့ 56132 ▁құжаттар 56132 ▁Lösung 56131 ▁waxaanu 56131 ▁தொழில்நுட்ப 56130 İB 56129 ૂટ 56129 ዌ 56129 ▁baduzu 56129 ▁السعودي 56127 ▁muassa 56126 ▁Επικ 56126 ▁زال 56126 gæng 56125 cmai 56124 ▁dokonce 56124 ▁uzsk 56124 ▁ការងារ 56124 vete 56123 edrag 56120 的方法 56120 ▁народи 56118 ▁саяса 56118 сним 56116 ադրության 56116 ▁زيادة 56116 ▁дому 56115 ▁تفصیلات 56115 ▁gesien 56113 غلي 56112 ouroù 56111 ത്തിയത് 56111 ▁Alder 56109 ▁ලිප 56109 ▁suerte 56106 ▁терро 56106 ▁สร้าง 56106 ▁szül 56104 يتون 56103 ▁Zone 56103 ▁jederzeit 56103 ▁Олег 56102 ▁Iru 56101 ▁դու 56100 ▁השלי 56099 ▁περίοδο 56098 nīc 56097 ▁Siin 56097 ▁slog 56097 ▁súil 56097 ▁Павел 56095 ▁oszt 56094 ▁gosta 56093 ▁дүрт 56093 ป่วย 56091 ▁poduj 56090 ▁45, 56088 ▁kläder 56088 ▁১৭ 56088 ▁വിപ 56088 казаць 56087 ▁आतंक 56087 ▁ljudima 56086 mála 56085 ▁ଭୂ 56085 hara 56083 mhar 56083 ▁kiinnost 56083 ▁εμφ 56083 라우 56083 ▁tzw 56082 ▁aurrean 56081 ▁sidder 56081 ▁महोत् 56081 ▁গ্রহ 56081 ▁ganga 56080 otten 56079 ▁dětí 56079 ▁gné 56079 публике 56078 ▁Hugh 56078 ▁inseamna 56077 ▁urządzenia 56077 سٹری 56076 วิธีการ 56076 ▁Aç 56074 ▁Vijeća 56074 ▁besplat 56074 ▁ընթաց 56074 hei 56073 udella 56073 άνε 56073 ท้าย 56072 ▁babak 56071 ▁studente 56071 ксы 56070 ▁Macedonia 56070 ▁meteissas 56070 ▁বই 56070 ingizni 56069 უჩ 56069 ▁ਫਰ 56069 çinin 56068 ▁stycznia 56067 ▁syks 56067 jekter 56065 ▁belast 56065 ▁ሲሉ 56065 něte 56063 ▁Plantilia 56063 utuhkan 56061 ▁legenda 56061 ▁Följ 56060 ▁player 56060 ▁مڙ 56060 ▁joul 56059 ▁propostas 56059 ▁رعایت 56059 ▁시즌 56059 ambolito 56058 ተት 56058 ▁halal 56057 ▁intéress 56057 ▁بالح 56057 ▁refr 56056 କ୍ର 56055 ▁Lih 56055 ▁fəaliyyəti 56055 ▁ಬೆಂಗಳ 56053 ▁dipilih 56052 ρίζεται 56051 ▁KID 56051 ▁frábær 56051 ▁kaikkea 56050 ▁Одно 56050 estii 56049 ေတြ႔ 56049 ▁миллионов 56049 ってきました 56049 ▁nemám 56047 ▁regal 56047 óne 56046 ционного 56046 ିତ୍ 56046 ▁altına 56046 ități 56045 ▁երեք 56045 teman 56044 кей 56044 ▁Joerhonnert 56042 årige 56040 ▁Vul 56040 ppel 56039 проект 56039 ▁benytt 56038 ▁Тогда 56038 ▁stampa 56037 ▁times 56037 ▁Христос 56037 ▁Rov 56036 ▁representar 56035 ▁tviv 56035 ▁Бургас 56034 ▁espec 56033 ▁zabud 56033 ▁evitare 56032 ▁įsp 56030 likud 56029 ▁Dance 56029 ▁темата 56029 alanya 56028 র্জ 56028 કારી 56028 ▁Perënd 56028 ▁Sales 56028 ▁ņem 56028 ▁lindo 56027 ▁cenah 56026 ▁voltam 56025 ▁секун 56024 ▁මෙන් 56024 color 56023 ▁cámara 56022 プレゼ 56022 ▁Rwanda 56020 ?”, 56019 ריז 56019 ▁sgil 56019 ▁vsako 56019 ▁вка 56019 зино 56018 ्वा 56017 ▁bau 56017 ђено 56016 ▁Vē 56014 ▁bosib 56014 ▁svake 56014 ▁väljer 56014 あら 56014 グループ 56014 ▁Dyma 56013 хилга 56012 ▁परंतु 56012 丰 56012 ilne 56011 ượt 56010 ▁Chuyên 56010 ▁Xorn 56010 ▁นี้ 56010 יש 56009 รู้จัก 56009 데이트 56009 riga 56008 ▁edizione 56007 ▁പ്രച 56007 جموعة 56006 ▁lön 56006 ▁แห่ง 56006 ▁ເຄື່ອງ 56006 czek 56005 ▁Inté 56005 дука 56004 بدو 56003 oratana 56002 egkeet 56001 ▁замов 56001 ▁زدن 56001 तान 56000 ▁prostried 55999 ▁tolle 55999 ábel 55998 ▁σκε 55997 ▁проявля 55997 ▁ಅರ್ಜಿ 55997 ▁Աբ 55996 ▁portes 55995 ▁võimalus 55994 ølv 55993 ▁przewod 55993 ▁የፍ 55993 小编 55993 ipro 55991 ▁İm 55991 ಗೋ 55990 ዲዮ 55990 ▁बच् 55990 ▁បញ្ហា 55990 ▁cala 55988 імі 55987 ▁Gere 55987 ▁gewuer 55987 följ 55986 шті 55986 ▁мотор 55986 ورات 55985 ▁şeklinde 55984 ▁ನೀಡಿದ 55984 ច្បាប់ 55983 ▁LEGO 55983 ▁faites 55983 легендә 55982 ▁залу 55982 stw 55981 ▁Hash 55981 ▁لأنه 55981 ▁वक्त 55981 žės 55980 ▁pamaké 55979 ▁tsj 55979 ▁автора 55979 ▁לילדים 55978 bron 55975 ▁περά 55975 תיד 55974 ▁кър 55974 ekonom 55973 orshe 55973 ▁šiem 55973 ìob 55971 ēšanās 55971 ▁гарсан 55971 ▁companaidh 55970 סור 55968 ▁Administración 55968 ▁siir 55967 ▁апара 55967 ▁гимназия 55967 ▁градот 55967 UNE 55966 ուրջ 55966 ▁camer 55966 ▁ممنوع 55966 pailu 55965 ▁Universität 55965 ▁seznam 55965 ▁κατασκευ 55965 ▁मंसिर 55965 ▁Islaam 55963 ▁hoit 55963 ▁palav 55963 มอบ 55962 ▁skel 55962 ▁ضل 55961 稍 55961 ▁mban 55959 engeziwe 55958 ▁Európa 55958 ▁MENG 55958 ▁виготов 55958 öva 55957 ▁Wach 55957 лыққа 55955 ▁tilu 55955 آباد 55954 ಾನು 55954 ▁dagok 55954 ▁šalių 55954 ▁sånn 55953 ▁yaxsh 55953 ▁próx 55952 ▁کلید 55952 体现 55952 ▁tartott 55951 ਪੂਰ 55950 ಹೆ 55950 ▁ct 55950 ▁splend 55950 ▁stic 55950 ▁względ 55950 ▁podróż 55948 ▁Redak 55944 ▁mafunzo 55943 Mak 55942 ▁Ukur 55941 ▁vlade 55941 ▁தந்த 55941 방법 55941 olako 55940 أي 55940 ▁Bakanı 55940 ▁مشابه 55940 lamak 55938 ▁איד 55938 ▁Romano 55937 ▁besig 55937 ▁سیا 55937 arrib 55936 endig 55936 ezeka 55936 ▁моря 55936 ▁ֆուտբոլ 55936 സിന് 55935 ංගුවට 55935 ارک 55934 ਮਤ 55934 ▁krag 55934 ▁משפחה 55933 можно 55932 ▁BRE 55932 ▁rúm 55932 ▁widać 55932 lenme 55931 ▁Bağ 55931 ▁scann 55931 ▁lembaga 55930 ▁മുസ് 55930 έργ 55927 ▁belieb 55927 ▁በተጨማሪ 55927 ▁ဘုရား 55926 ▁ແອັຟ 55925 සික 55924 ▁പറയുന്നത് 55924 ellir 55923 ▁կանոն 55923 製作 55923 ಂತಿ 55922 ▁NIE 55922 ▁रोजगार 55922 fication 55921 ▁втора 55921 ▁안녕 55921 igrant 55920 ოსი 55920 Kita 55919 ▁Look 55919 었고 55919 ▁Аднак 55918 뿐 55918 ▁അടു 55916 uhlangana 55915 ▁sà 55915 ศา 55914 ▁қорға 55912 ▁MÜ 55911 ▁ڪوٽ 55911 เปล 55910 ▁ampiasaina 55910 ▁SOM 55909 opian 55908 мове 55908 ▁cath 55907 ▁결혼 55907 емой 55905 ▁ಉತ್ಪ 55905 trica 55904 ▁titta 55904 ciou 55903 ▁halus 55903 ▁heinäkuuta 55902 ▁velikosti 55902 ▁Ҡыр 55902 ▁Paolo 55901 ▁فقير 55901 ভব 55899 ▁दलित 55899 ▁ດັ່ງ 55899 ққан 55898 ▁aia 55898 ▁engozini 55898 ithredu 55897 วจ 55897 ▁Podczas 55897 एँ 55896 ▁menciptakan 55896 ▁mirar 55896 једиње 55895 ▁જાણી 55895 可以在 55895 тала 55894 ▁iekš 55894 과의 55894 νομα 55893 wija 55892 ▁piedi 55891 ▁vaccin 55890 ▁թիմ 55890 lopp 55889 Ợ 55888 ▁кіно 55888 љима 55887 家里 55886 杂 55886 ▁Paper 55885 ierlech 55884 ikel 55884 ▁teljesítmény 55884 ▁terminar 55884 Program 55883 erske 55883 észség 55882 ▁njihova 55882 ▁pouvwa 55882 ▁мани 55881 ▁იტ 55879 änen 55878 ▁һеҙҙең 55878 ▁დასახ 55878 hengig 55877 ▁utilizado 55877 ▁միլի 55877 ▁जवळ 55877 droš 55876 ▁ndege 55876 ▁kõrval 55875 Ru 55874 ▁chwazi 55874 ▁εφημερί 55874 ▁בקי 55874 ▁کامپیوتر 55874 สเตอร์ 55873 úm 55871 ▁bisi 55871 ▁script 55871 ▁чадвар 55871 ▁کوئ 55871 ▁determinar 55870 ▁diku 55870 ▁mjesec 55870 ▁resultaat 55870 ▁облысының 55870 ▁Kors 55869 ▁مریم 55868 ertz 55867 ▁razdob 55867 ▁skär 55867 ▁kommenterar 55866 固定 55866 owaną 55865 jant 55863 рки 55863 ကြယ္ 55863 ▁gegee 55863 istiske 55862 ÉK 55862 انيا 55862 ▁kellene 55862 ▁ສົ່ງ 55862 ▁dumneavoastra 55860 ▁несмотря 55860 ▁Akụkọ 55858 ▁أساس 55858 ▁جست 55858 ▁these 55857 ▁ביים 55857 ▁kontroller 55855 -196 55854 abhäng 55854 ▁असल्याचे 55854 ▁kupić 55853 ▁ପ୍ରଭାବ 55852 اريع 55851 ▁Vielleicht 55851 ▁HK 55850 ▁നേര 55850 ▁(' 55849 ▁матеріа 55849 ▁inimene 55848 ▁आख 55848 remen 55846 եծ 55846 ▁sosti 55846 mohonan 55845 ínio 55845 ▁Afirka 55845 ക്കാർ 55843 ▁amateur 55843 ▁gắng 55843 に対する 55842 ▁namung 55840 ▁topic 55840 ▁потпи 55840 ln 55839 obail 55839 ▁AU 55839 ▁Platform 55839 cato 55838 مرة 55838 ▁máli 55837 ▁başlad 55836 ▁giusto 55836 儿子 55836 ▁galegas 55835 ▁Защо 55834 ▁chcesz 55833 ▁خوند 55833 ▁dk 55832 ratis 55831 ▁сајт 55831 ظة 55830 ▁tounen 55829 ▁zunan 55828 ▁संस्कृति 55828 கவ 55827 หลอด 55827 ▁administras 55827 ▁Павло 55826 ▁усво 55825 Bro 55824 ▁düşür 55824 ▁područ 55824 老師 55824 ▁06, 55823 ▁พวก 55823 ababisha 55822 mā 55822 ījis 55822 êts 55821 ▁ຕິດຕາມພວກເຮົາ 55821 提供了 55821 တင်း 55820 ្ជាប់ 55818 ▁-8 55818 éirí 55817 atschapp 55815 ▁Moda 55815 ल्पा 55814 ▁Yıl 55814 ▁القاد 55814 대를 55814 ▁претход 55812 ▁(!) 55811 ▁dezhañ 55809 ▁kullanılan 55809 ▁oftast 55809 ▁stranici 55808 ▁Джо 55808 عدام 55807 ▁Hizmet 55807 ▁Meine 55807 EBO 55806 alq 55806 ▁rối 55806 raich 55805 ▁ອາຣ໌ 55805 ordet 55804 rnat 55804 зіргі 55803 ▁sungguh 55802 ▁tambíen 55802 ğraf 55801 БР 55801 ▁ανάρ 55801 ▁체험 55801 ເລັດ 55800 ▁פארשט 55800 ▁સાંભ 55800 ZY 55799 ▁cht 55799 ▁تړون 55799 ▁ສໍາຫລັບເຂົ້າຫາ 55799 担心 55799 endez 55798 ▁tantaran 55798 ุณา 55797 ▁энергия 55797 ▁پیچ 55796 lande 55795 ▁enz 55794 ilərin 55793 ▁keuken 55793 ▁نوازش 55793 ▁infant 55792 ▁սահմանված 55792 人大 55792 dest 55791 attia 55790 ▁বৃহস্পতিবার 55790 ପାରିବେ 55789 ▁posibilidades 55789 ▁pund 55789 आय 55788 ▁аа 55787 ▁წერ 55787 ってもら 55787 ▁görür 55786 ▁modne 55785 ▁pese 55785 ▁مسئلہ 55785 几年 55785 ▁վստահ 55783 watir 55782 ▁власності 55781 adaki 55780 ciato 55780 យ៍ 55780 ▁diwrnod 55780 álacha 55779 ▁42, 55779 ▁señor 55778 ▁ном 55777 ércoles 55776 ▁maikadua 55776 ▁şans 55776 也能 55776 ▁ପୋଲିସ 55775 ▁imaxes 55774 ▁parand 55774 ▁tinta 55774 เจริญ 55772 ▁Kasar 55772 ▁laro 55772 varing 55771 ▁Ured 55771 ▁detí 55770 ▁italiane 55770 ▁ඉතිං 55770 ▁fréttir 55769 ▁necesaria 55769 ▁nápad 55768 ▁सामना 55768 osl 55767 oterapi 55767 ▁לבין 55767 ▁의료 55766 izzare 55765 kuasa 55765 ▁Eropa 55765 ▁esiint 55765 ▁СМИ 55764 ▁የተለ 55764 ziteta 55763 ंबर 55763 ผู้ชาย 55763 ▁halál 55763 ▁उद्ध 55762 ▁dejlig 55761 ▁обслугову 55761 ▁നിയ 55760 ▁ይልቅ 55760 ▁Compet 55759 qand 55758 ▁seek 55758 ▁seirbhís 55758 ▁ελεύθε 55758 ▁ути 55756 ▁ಬಹಳ 55756 ienka 55755 ▁тирә 55755 ▁nəzərə 55753 ▁მეც 55750 pper 55747 ▁հատկ 55747 ščine 55746 คาด 55746 ▁wasiir 55746 éttir 55745 किल 55745 ▁staf 55745 ▁अलावा 55744 Jer 55743 vidas 55743 ▁gebou 55743 ▁ਬਾਦਲ 55743 浦 55742 ▁cef 55741 ▁อันดับ 55741 ▁prene 55740 ▁tøj 55739 防止 55739 ▁πρωτα 55738 ▁Dofir 55737 آور 55736 ▁غربی 55736 koli 55734 ▁szerződés 55734 ▁veľký 55734 ▁imit 55733 itasi 55732 ရည္ 55731 व्यव 55730 აზი 55730 ▁Río 55730 ▁waka 55730 ▁സമീപ 55730 έντε 55729 േയും 55729 ▁Donc 55729 zol 55728 ▁boldog 55728 ▁தள 55728 wicklung 55727 ▁Bax 55727 ▁solt 55727 ▁Στις 55727 ▁보는 55725 bring 55723 ຖື 55723 ▁politiku 55723 ولد 55721 ▁entid 55721 ▁prestigi 55720 满意 55719 wasili 55718 ▁evang 55718 ▁mpik 55718 ▁mokes 55717 ▁אַנדער 55717 ▁ಕುಟುಂಬ 55717 ▁Guest 55716 ▁seun 55716 λούς 55715 ▁Monate 55715 께서 55715 loven 55714 ▁βοή 55714 induzi 55712 مدينة 55712 ugun 55709 ▁ହୁଏ 55709 ренсе 55708 ตี้ 55708 ▁alar 55708 ▁സാധ്യ 55708 ▁председника 55707 ▁አሸ 55707 मित 55706 ▁Heraus 55706 ▁Sven 55706 oloana 55705 дердің 55705 ▁ኮሚቴ 55705 ส่วนลด 55704 ▁இஸ் 55704 ▁health 55703 ▁german 55702 ▁ਗਿ 55702 ▁മാറി 55701 onao 55700 ▁huunde 55700 ▁působ 55700 ոնական 55699 ▁الأوروب 55699 ▁სარ 55699 ▁budi 55697 melig 55695 ¶ 55694 ▁নার 55694 glaidh 55693 øyd 55693 ▁допъл 55693 ▁webových 55692 ▁ทีม 55691 ▁Král 55690 ▁avanti 55690 gbẹ 55689 isich 55689 ▁شدم 55689 sgál 55688 urgia 55688 ▁ווייַ 55688 ikazi 55687 ▁față 55687 ▁اجتماع 55686 ڙيل 55685 ▁bantfu 55685 ▁koyeba 55685 ▁حامد 55685 ▁ओळ 55685 立即 55685 ρικές 55683 ▁qëllim 55682 經驗 55682 минал 55681 ▁ڪون 55681 労働 55681 ▁സ്ഥല 55680 beed 55678 ▁wysy 55678 ԱԾ 55677 stin 55676 wangun 55675 ▁eleccions 55675 ▁հանդիս 55675 ▁일을 55675 ▁León 55674 ▁asim 55673 加盟 55673 ▁проектов 55672 ▁trovato 55671 ▁végül 55671 ▁хло 55671 ▁परा 55671 าบ 55670 ເວັບ 55669 ▁приш 55668 ▁своєї 55666 ▁εσωτερ 55665 ▁Máis 55664 ▁partits 55663 ▁usluge 55663 ▁προσε 55663 ▁dinh 55662 ▁mą 55662 ីវ 55661 ▁Jés 55661 oned 55660 ITU 55659 уап 55659 ▁Правосла 55659 ▁عطا 55658 ▁फरक 55658 ออกไป 55654 ▁hoi 55654 ▁περιε 55654 ▁капи 55654 ▁amawebhusayithi 55653 роден 55650 язково 55649 niti 55648 qich 55648 ▁strādā 55648 Tag 55647 lék 55647 ▁କ୍ଷେତ୍ର 55646 جهو 55645 ▁unib 55645 ▁(*) 55644 ▁damo 55644 ▁premis 55644 ▁kanser 55643 ▁ngezinhlobo 55643 ረስ 55642 ▁tartış 55642 ▁koks 55641 Donald 55640 ይታ 55639 ▁ಅನುಭವ 55638 ▁Constantin 55636 ▁testen 55635 energ 55634 ▁Anderson 55633 ▁идни 55633 ▁leutik 55632 ▁précéd 55632 ▁ניהול 55632 ▁سروس 55632 ▁durumu 55631 ▁sb 55629 ▁अझै 55628 ▁गोली 55628 ▁apg 55627 100% 55626 ▁İmam 55626 ೋತ್ಸ 55625 ▁acontecer 55625 ▁316 55624 ▁Buz 55624 ▁musamman 55624 ▁tutur 55624 ▁անկ 55624 ቹን 55623 ▁vrsta 55623 ▁ખબર 55623 SEO 55620 шэнне 55620 ▁olingan 55620 щиков 55619 ▁Rasul 55619 want 55618 úcar 55618 ▁Safari 55618 ▁teulu 55618 ▁салбарын 55618 ▁բռն 55618 ▁کرمان 55616 ▁Carles 55615 ▁забезпе 55615 asatan 55614 ▁निधन 55612 agers 55611 epp 55611 orana 55611 ▁cadas 55611 ▁osnovu 55610 ▁ריי 55610 ▁hovorí 55609 ▁কু 55609 amiseksi 55607 ustand 55607 ▁knih 55607 iening 55606 ▁ریز 55606 一致 55605 成就 55605 ાયે 55604 ▁Þor 55604 രിക്കാന് 55603 ▁अधिकृत 55603 ▁Train 55602 ▁päätö 55602 etil 55601 zeich 55601 ▁kodėl 55601 ▁atividade 55600 ▁erabaki 55600 ▁земјата 55600 lawanan 55599 ▁mübarizə 55599 ▁үүрэг 55599 eskedel 55597 ▁chòir 55597 ▁süreç 55597 ▁комитеты 55597 ▁Kí 55595 ▁प्रार 55595 ▁อัน 55595 líben 55594 Кра 55594 жний 55594 ▁služeb 55593 نداسين 55592 社の 55592 ▁squadra 55591 ▁오전 55591 ▁maqol 55590 ▁İki 55590 ▁oluline 55589 ▁ensikl 55588 ▁ساعات 55587 átase 55586 ûnder 55585 ▁କ୍ର 55585 メラ 55585 沃 55584 தற்கு 55583 ▁zime 55583 ▁megér 55582 ▁կարծիք 55582 ▁নিরাপ 55581 ▁suoj 55580 ▁қалыптастыру 55580 แพ้ 55579 ▁designa 55579 ▁салу 55579 ▁сират 55579 ▁sección 55578 ▁tio 55578 ▁vrijdag 55578 鄉 55578 naidheachd 55576 ukuthela 55576 Lwes 55575 fisk 55575 ▁αυτοί 55575 ▁멋 55575 говой 55573 Afr 55572 ▁yere 55572 ▁összet 55572 ინე 55571 comm 55570 ▁урман 55570 ▁അല 55570 ītāju 55569 ▁Guz 55569 ▁regl 55569 ାମାନ 55568 ▁rakt 55568 IDS 55567 රිත 55567 ქართულად 55567 ▁інтернет 55567 ouvri 55566 érieure 55566 ▁эсрэг 55566 ▁fì 55564 ▁izinkinga 55564 ▁makubwa 55563 ▁ulko 55563 ▁σπου 55563 ಲಿನ 55562 ▁поу 55562 जों 55560 ▁vuur 55560 itara 55559 ▁Lenovo 55558 ▁využív 55558 ▁паж 55558 ▁ഓഫ് 55558 ▁Wyn 55557 zań 55556 ወር 55556 ▁vergangenen 55556 rivere 55555 ▁dvi 55555 ▁lucah 55555 サイ 55555 GM 55554 arnen 55554 ▁Pirenèus 55554 ▁Reserv 55554 ▁hotels 55554 ▁domenica 55553 тле 55552 ▁õht 55552 alía 55551 uß 55551 ▁앱 55551 ▁Hlavní 55550 ▁வேண்டு 55550 ▁Мінску 55547 ▁نشد 55547 ▁konkrete 55546 ▁нэвтр 55546 ୁରୋ 55545 ▁Jedna 55544 ▁możliwe 55544 ▁Tort 55543 ▁tercera 55543 ▁бизнис 55543 usahaan 55542 ▁کردیم 55542 ▁හිමික 55542 pení 55541 ulier 55541 نسي 55541 ▁اداري 55541 ▁ይች 55541 ▁njoh 55540 ibatkan 55539 κρο 55539 ▁máquina 55539 ▁बरा 55539 vata 55538 ଦ୍ଧାର 55538 ▁Hyper 55538 ▁átlag 55537 ukio 55536 ▁jelleg 55536 ▁злоу 55536 ▁تفاوت 55536 ▁शुल्क 55535 oshta 55533 ▁Arewa 55533 ▁dedicada 55533 ▁fizik 55533 ▁presque 55533 ▁બ્લો 55533 ▁gasi 55532 ▁geçer 55532 ▁обеща 55532 ▁הדבר 55531 чык 55529 ▁seriá 55529 ዑ 55528 ▁intsony 55528 郑 55528 შა 55526 障害 55525 ▁taigh 55524 ▁Nema 55523 ▁ಅಭ್ಯರ್ಥ 55523 ▁правото 55522 ▁वर्षे 55522 ▁sonrası 55521 ▁hjælper 55520 straat 55519 就會 55519 haigh 55518 ▁kurią 55518 宗教 55518 ▁դրանք 55517 ▁llabor 55516 看来 55516 ▁नियन्त्रण 55515 ▁Γρα 55514 ▁kullanım 55513 ▁무단 55513 ▁Mäng 55512 ڊر 55511 ▁галоў 55511 брой 55510 ▁Politica 55510 ▁(2015) 55509 ▁језика 55509 ▁dysgu 55508 ▁gestellt 55508 ▁स्टार 55508 ▁харил 55507 ▁ضلع 55507 ▁nalang 55506 特点 55506 拖 55505 йсці 55504 ▁Shan 55504 ▁butang 55504 ▁खुश 55504 áka 55503 ▁Gaborone 55503 ▁يک 55503 ▁Нэг 55502 ћем 55501 ▁Nwere 55501 ▁Xxx 55501 ▁nəzarət 55500 ▁sibuk 55500 ▁хүчний 55500 āmas 55499 արանում 55499 ▁Aqui 55499 ▁স্প 55499 цкий 55498 ▁bahagia 55498 ▁влезе 55498 ▁ইস 55498 ▁ஜோ 55498 幸せ 55498 чево 55496 телә 55495 ▁Národ 55495 ▁فه 55494 דוש 55493 чниот 55491 ▁pengene 55491 ▁sunda 55491 45, 55490 ப்படுகிறது 55490 ▁nyhetsbrev 55489 show 55488 ▁participat 55488 ▁reality 55488 ▁کاربر 55488 ▁Quran 55487 ▁продуктов 55485 が高い 55485 stige 55484 ėme 55484 ▁Volks 55483 ▁muista 55483 ▁тақырып 55483 ▁அவன் 55483 ◄ 55483 φέρουν 55482 ▁זע 55482 ▁sèt 55481 ▁damar 55479 ▁leader 55479 ▁Согласно 55479 ▁szavaz 55478 ўнік 55477 ▁გენ 55477 ▁relació 55476 ▁sood 55476 ▁słowa 55476 收到 55476 雇 55476 klju 55474 ▁Kazakh 55474 ▁domhan 55474 ▁loạn 55474 ▁۲۳ 55474 ▁ота 55473 дност 55472 ▁Mengen 55472 ▁Taarifa 55472 ▁nyak 55472 ▁პერიოდ 55472 ▁dlm 55471 ▁vlad 55471 ▁íz 55471 भो 55470 ▁مرداد 55470 兄弟 55470 emment 55469 Ổ 55469 んじゃない 55469 윈 55469 ▁Nasıl 55467 ▁להשתמש 55466 ▁главата 55465 ▁повітря 55465 тод 55464 miet 55463 ชื่น 55463 ▁Všechny 55462 ▁bharrachd 55462 ROU 55461 ▁epekto 55461 ▁vitt 55461 ▁সাধারণ 55461 ▁gjendje 55460 ▁jare 55459 kiinto 55458 look 55458 ▁oppos 55457 ▁Щоб 55457 ທ່ອງທ່ຽວ 55456 ▁Бен 55456 говорите 55455 чала 55454 ពល 55454 คอมพิวเตอร์ 55450 ▁дамы 55450 ▁хоно 55450 ▁முற 55450 ▁ေပး 55448 жур 55447 ▁बातें 55447 onyaka 55445 ▁آذرب 55445 ▁Tử 55444 ▁Tunn 55443 ▁космо 55443 ▁jedhan 55442 opul 55441 жних 55441 makta 55440 rotnie 55440 ▁206 55440 ▁губи 55439 пень 55438 ▁aģ 55438 ▁ኢትዮጵያውያን 55438 رسی 55436 ជ្ 55436 ▁Prés 55436 anareo 55435 URAN 55434 ୨୦ 55434 ▁skolē 55434 ▁syv 55433 ryn 55432 Rev 55431 ▁закладів 55431 ▁Հակ 55431 ▁lanaw 55429 ▁ପ୍ରଶା 55429 ѐ 55428 ▁Μαρτίου 55428 ▁پرمخت 55428 ਜ਼ਾਦ 55427 ▁chociaż 55427 ▁konum 55427 ләндереү 55426 ೕ 55426 ▁thirr 55426 ▁تلا 55425 ▁esib 55424 ▁الفلسطينية 55424 ▁autors 55422 ▁kohas 55422 ▁necessità 55422 ▁տնտես 55422 ▁saus 55421 ▁tatsächlich 55421 odadh 55420 стэ 55420 шҡор 55420 ▁Huk 55420 进口 55420 عليم 55419 ▁coeur 55419 ▁башлана 55419 ▁нере 55418 klam 55416 ▁Reyk 55416 ▁gje 55416 ▁veikl 55416 ตั๋ว 55415 ▁dijelu 55415 ▁İc 55415 ▁Ame 55413 ▁தேதி 55412 少ない 55411 ▁csökkent 55410 Next 55409 ূহ 55409 ห่าง 55409 ▁améli 55409 ുമു 55408 ▁старшы 55408 ▁재미 55407 ಗೇ 55405 ▁қойы 55405 ▁ದಕ್ಷಿಣ 55405 ▁სისხ 55405 แห่งชาติ 55404 ọa 55404 ▁شمیر 55404 ▁پیپلز 55404 ▁Pravil 55403 then 55402 wiad 55402 ▁mahad 55402 ▁parke 55402 ▁pomočjo 55402 ともに 55402 quoi 55401 ▁dagaalka 55401 ▁pewnością 55401 cond 55400 ▁Wett 55400 iritu 55399 ▁Hib 55399 ▁adre 55399 nė 55398 víz 55398 òme 55398 ▁ከነ 55398 ecken 55397 ▁erklärt 55397 ▁ماہر 55397 ▁հնարավորություն 55396 ▁ukulahlekelwa 55394 ▁पद्ध 55394 ▁પાંચ 55394 သို့ 55393 ituko 55391 leitung 55390 夠 55390 ▁Тал 55386 торыя 55385 ▁पूरे 55385 ▁මුහුණ 55385 ▁ຍິງ 55385 owoo 55384 ▁biss 55384 ▁مرک 55384 ಲ್ಲೇ 55383 ▁Mahathir 55383 ▁perdere 55383 ▁мени 55383 formo 55382 ▁mahusay 55382 ▁развива 55382 ▁स्वी 55381 ▁ചെറിയ 55380 ▁انھوں 55379 ▁ٹرمپ 55379 ▁dopln 55378 ▁miesięcy 55378 ▁resevwa 55378 ▁menjalani 55377 ▁তৈরি 55377 ویس 55376 ▁ellenőrz 55376 otšišo 55375 ▁zní 55373 ▁затова 55373 amientas 55372 以降 55372 ▁CONT 55370 ▁ڪافي 55370 ात 55369 ▁COP 55369 ರಿಕೆ 55368 ǹ 55367 သွား 55367 ▁പ്രഖ്യാപ 55367 ស្រុក 55365 ପାଦ 55363 ▁Köln 55363 ▁dlouho 55363 իզնես 55362 କ୍ସ 55360 මම 55360 ▁Lily 55360 ▁βρέ 55360 ▁экономики 55359 idah 55358 trice 55358 ▁BAY 55358 ▁այստեղ 55358 ▁احتجا 55358 kuji 55357 ▁smješ 55357 ▁Коро 55357 irio 55356 μενο 55355 ಾಂಶ 55355 ▁Обя 55355 ▁бичиг 55355 gning 55353 ěru 55353 ▁dàta 55352 ▁решил 55352 ▁කරල 55352 ▁стри 55351 ▁câte 55350 avsett 55349 prob 55349 ଖ୍ୟା 55349 ▁Tilmeld 55349 ▁blads 55349 ▁Републи 55348 χημα 55347 ▁Lietuvą 55347 ▁Warri 55347 sicher 55346 ▁гіпер 55345 ▁Masar 55344 ▁poziomie 55343 ▁አሜሪካ 55342 amál 55341 ▁જેટ 55341 ▁ବସ 55341 праве 55339 ▁ټرمپ 55339 ▁ڪٿي 55339 ▁vento 55337 15) 55336 ▁байгааг 55336 ▁автори 55335 ▁வேறு 55335 わず 55335 voerd 55334 ▁وهم 55334 ▁पृथ 55334 ▁sill 55333 ▁мов 55333 liste 55332 θω 55332 ▁現在 55332 .04 55331 ▁khuôn 55331 ▁soient 55331 ▁völl 55331 ▁itan 55330 ▁luxus 55330 eddol 55326 ▁ঘোষণা 55326 ▁moog 55325 น้ําตาล 55324 ▁Naxçıvan 55324 各项 55323 וצרים 55322 ▁могло 55322 ▁বান 55322 ▁borger 55321 ▁اکين 55321 дові 55320 ▁Kế 55320 ▁ተገ 55320 ▁fotky 55319 ▁шеф 55319 чкі 55316 ▁uitg 55316 ▁বিমান 55315 где 55314 ▁мережі 55313 椅 55313 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54265 ರಾಗ 54264 ▁पंत 54264 ▁สง 54264 ▁꾸 54264 стами 54263 ▁Dug 54263 ▁Enrique 54262 ▁япон 54262 องค์กร 54261 ▁выво 54260 ▁договору 54260 ogrāf 54259 ▁Finance 54259 ▁الی 54259 ▁آشن 54258 ▁sinda 54257 คัน 54256 ▁Sae 54256 ▁rull 54256 ▁title 54255 છી 54253 ▁მუხ 54253 ▁Uniberts 54250 ▁ćwic 54250 上升 54250 üística 54249 ▁skaičius 54249 маса 54248 ▁bocah 54248 ရြာ 54247 ▁Alternativ 54246 ibiliz 54245 ▁ຮູບ 54245 ▁pavadin 54244 ▁xavfs 54244 ▁քաղաքացի 54244 ▁توضیح 54244 teilung 54243 ▁Voz 54243 ▁Վրաստ 54243 ▁ډی 54243 թուր 54242 ▁negativa 54241 ▁مسعود 54241 imiy 54238 ▁verrà 54238 ▁Ֆրանս 54238 ▁bashku 54237 ▁دخل 54237 fies 54235 ▁وتم 54235 wyddiad 54234 ▁використа 54234 toh 54233 àrr 54232 ▁έκα 54232 лоди 54231 ▁ନିଆ 54231 kahwinan 54230 poko 54230 ▁Định 54230 ▁commune 54228 pó 54226 ▁vije 54225 ▁γλώ 54225 ▁айында 54225 ▁กล่าว 54225 ▁สมัคร 54225 portal 54224 ämättä 54224 КІ 54224 ▁najviac 54224 ▁חיפה 54223 ▁लहान 54223 িয়ন 54222 ▁wirtschaft 54222 ▁Căn 54221 ▁Hivat 54221 ▁മരണ 54221 90. 54220 ▁Hozz 54220 စည္း 54219 oble 54218 良好 54217 ▁chambres 54216 ▁ћемо 54216 ▁معاشر 54216 ▁등에 54216 zkou 54215 ▁anta 54215 ▁પૂછ 54215 vain 54214 ▁університету 54214 ▁Geschäfts 54213 ▁britán 54213 ▁wiedzy 54213 ▁شہباز 54213 ▁linii 54211 ▁შეგიძლიათ 54211 ,3 54210 ▁Gep 54210 ▁NAV 54209 ▁atribucion 54209 плом 54208 ▁Dél 54208 ▁একজন 54208 ŷ 54207 ▁Müdürü 54207 ▁ලකුණු 54207 해보 54207 ▁mestre 54206 ▁צורך 54206 ▁کړکۍ 54206 ▁avval 54205 ължи 54204 ▁δαν 54204 cznej 54203 ▁Bangladesh 54203 alse 54202 ▁Gitt 54202 ▁საინტერესო 54202 υναι 54201 ▁anch 54201 ▁parter 54201 ungguh 54200 ▁collection 54200 로운 54200 isuuf 54199 änga 54198 ুব 54198 ▁sover 54198 ▁vajz 54198 ▁imposible 54197 ▁Democrat 54196 ▁rekre 54196 ▁teile 54196 ▁ແຜນ 54196 uranter 54195 វៀតណាម 54195 ▁vél 54195 ▁તારી 54195 ▁rondom 54194 endeu 54193 ▁ਅਪ 54193 跡 54193 ▁socialista 54192 ▁dabas 54191 ▁nguurndam 54191 jungos 54190 102 54189 ▁גבי 54189 ▁ಕಾಲೇಜ 54189 ▁ജ് 54189 ▁அணி 54188 arena 54187 ▁Händ 54187 ▁факул 54187 ▁تال 54187 ▁मुस्लिम 54187 ahanap 54186 र्च 54186 ▁engelska 54186 ▁subotu 54186 ▁الطفل 54186 ▁விலை 54185 xassis 54184 עריכת 54184 ▁بیمار 54184 ▁પૃ 54184 intean 54183 urian 54183 ▁Mondi 54183 ▁drod 54182 ပဲ။ 54181 ▁Masalah 54181 ▁planen 54181 ▁syns 54181 流行 54181 lende 54180 ▁ових 54180 ▁ເພີ່ມ 54180 ▁Серед 54179 ▁déro 54178 ▁klubben 54178 ▁խորհ 54178 ▁परेका 54178 حاق 54177 ▁вое 54177 ▁ຮັກ 54177 ▁Christopher 54176 관계 54176 മെന്ന് 54175 ▁sozialen 54175 jącego 54173 ▁thù 54173 ▁الحكم 54173 рер 54172 чилсан 54172 âmb 54171 ▁według 54170 ▁Слова 54170 ▁beskriver 54167 irl 54165 ത്താ 54165 ▁pajennoù 54165 ▁senda 54165 esneg 54164 ▁Thể 54164 ものの 54164 पटा 54163 लिस 54163 ▁problemy 54163 ▁الفا 54163 ▁வேண்ட 54162 ▁uppdater 54161 ▁dør 54160 ▁ronder 54160 ▁ellenére 54159 ▁көтә 54159 ▁alleg 54158 чога 54157 ▁πρώην 54157 ▁युद्ध 54157 ▁контроля 54156 ໂມ 54155 ▁γρήγο 54155 ▁ხელო 54155 ▁trauc 54154 ประทาน 54152 ▁ريال 54152 ▁جوش 54150 odată 54149 ▁económicos 54149 ▁tyl 54149 ▁чест 54148 ▁iedere 54147 ▁टेस्ट 54147 ▁መፍት 54147 ▁Dd 54146 ▁PKK 54145 ▁želimo 54145 מנה 54144 ▁mehnat 54144 ▁ويس 54143 ▁ទុក 54143 issements 54142 ▁κλεί 54141 ▁ਵਿਕੀ 54141 лыса 54140 ▁cyfr 54140 ▁Pueb 54139 сора 54138 ▁נושא 54138 ▁sëmund 54137 EUR 54136 ▁kayak 54136 ಬ್ರ 54135 ▁famig 54135 ▁relacionadas 54135 éder 54133 gleich 54132 ▁दुवै 54132 ▁mieh 54130 ▁websites 54130 ▁bezu 54129 ▁vamp 54128 ▁поез 54126 nuteľ 54125 ▁pál 54125 းဇူး 54124 ▁mennen 54124 ▁noto 54124 ▁nəqli 54124 ▁terceiro 54122 ▁Fundació 54121 ▁dhun 54121 ▁نقطه 54121 ดีกว่า 54120 ▁hash 54120 ▁ಪ್ರಯತ್ನ 54120 ▁показва 54119 седник 54118 ▁erfü 54118 ▁Ilma 54117 ▁පැහැදිලි 54117 ivre 54116 ύει 54116 ▁Arrim 54116 滋 54116 abagatan 54114 ▁කල් 54114 ▁Gallout 54113 ▁verilir 54113 ▁נוב 54113 önyör 54112 หมู่ 54112 ▁เอา 54112 渠 54112 ▁TRA 54110 ▁знайти 54110 ▁situatie 54109 чиков 54108 cionin 54106 ▁зама 54106 ▁žiadne 54105 ▁सवारी 54105 ▁Product 54104 ▁responsável 54103 ▁gacanta 54102 ▁முயற 54102 مث 54100 ▁تقریب 54100 ▁பெண்கள் 54100 ▁කොටස 54099 ▁drejtë 54098 тица 54097 יק 54097 ▁шанс 54097 ▁Jaal 54096 ਹਾਂ 54095 ▁stří 54095 ▁वर्षी 54095 ▁NEG 54094 ▁jobba 54093 ทรั 54092 ▁המידע 54091 ▁저희 54091 നോട് 54090 ▁Electric 54090 ▁Segon 54090 ▁वाचा 54089 eltes 54088 ▁kolejny 54088 ▁केपी 54088 ▁ઘટના 54088 ▁Elimu 54087 ▁پناه 54086 embali 54085 नेछ 54085 óma 54083 ҙарға 54083 ▁aberto 54083 ▁uomo 54083 107 54082 ▁православ 54082 ▁служе 54082 ▁الأمريكي 54082 ▁umano 54081 ▁затворе 54081 ଣ୍ଡି 54080 ▁ავტ 54080 eksa 54079 विण्यासाठी 54079 ▁ناول 54079 ferd 54078 ▁ခံ 54078 ઉન 54076 ચ્ 54076 wirk 54075 ▁phà 54075 ▁tantas 54075 štis 54074 ราม 54074 لاقة 54073 បំផុត 54073 ▁dúirt 54073 ocie 54072 ▁Cổ 54072 或许 54072 ▁espan 54071 ▁white 54071 ▁какое 54071 ▁הדר 54071 ▁beschäf 54070 кистан 54069 ▁Mynd 54069 ▁фру 54069 ▁Vuk 54067 ▁neile 54067 zif 54066 ▁unya 54065 ▁lekin 54063 ▁മമ്മ 54060 ▁다운로드 54060 ükl 54059 ਨੀਆ 54059 ▁izvješta 54058 ▁soss 54058 iloli 54057 ▁ninguém 54057 izik 54056 ▁Berl 54056 ▁متوجه 54056 γές 54055 ▁documental 54053 ▁дать 54053 więcej 54052 ुका 54051 ▁felul 54051 ▁umk 54051 ▁συνδυ 54051 ▁Risiko 54050 ▁acompanh 54050 ▁եվ 54050 ▁pohybu 54048 ▁ЈЕ 54048 ▁කාලේ 54048 ▁ժամկետ 54047 ▁სხვ 54047 톤 54045 ▁Цена 54044 eshayo 54043 幣 54043 ▁unterstüt 54042 žai 54041 ▁indre 54041 査定 54041 ▁Navo 54040 ▁Premiu 54040 ▁napak 54038 jegyzés 54037 ักษ์ 54037 ▁разполага 54036 ▁faer 54035 ▁ووژل 54035 ţat 54034 çev 54033 ▁ಸಿದ್ಧ 54033 上述 54032 ಿಯಾಗ 54031 ▁fékk 54030 を楽し 54030 ▁Boden 54029 ▁सारे 54029 ▁consecuencia 54028 ▁केवळ 54027 ▁മാത്രമല്ല 54027 тэт 54026 বির 54026 ▁corrent 54026 ▁opł 54026 ▁Nieder 54025 ▁društven 54025 ທະວີຕເຕີ້ 54024 ▁Juk 54024 ▁livres 54024 ▁pomiędzy 54024 ແລ 54023 ▁яу 54023 ▁cít 54022 ▁название 54022 地區 54022 ikle 54021 бую 54021 ▁Kaduna 54021 ▁unidade 54021 ▁anbefal 54020 ▁pirmin 54020 ▁polskich 54020 ▁supr 54020 vora 54019 ▁моей 54019 访问 54019 ಿಯಾಗಿ 54018 ▁rừng 54018 yayari 54015 ھار 54014 ▁aholi 54014 ▁σημεριν 54014 ▁געק 54014 rtasida 54013 რობს 54012 ▁travn 54012 嬉しい 54012 ▁otsa 54011 ▁taler 54011 سكري 54010 ላሽ 54010 ▁αυτών 54010 ▁серия 54010 ▁ақпараттық 54009 するのは 54009 arraí 54008 zelfde 54008 ▁Izao 54008 ▁нис 54008 ура 54007 omás 54006 ไปที่ 54006 ▁تناول 54006 ▁tinc 54005 ▁өңі 54005 ▁साधारण 54005 ▁२०१७ 54005 iapa 54004 рува 54004 ecture 54003 ▁Vih 54003 ▁pozdě 54003 ▁stjórnar 54003 ▁нацыяналь 54003 과정 54003 ▁keseluruhan 54002 ographi 54001 ▁ολόκλη 54001 ▁атра 54001 ▁ірі 54001 ଗୁଡ଼ 54000 ọpụta 54000 飯店 54000 ▁kollektiv 53999 धरी 53998 ▁cass 53998 əsidir 53996 年龄 53996 ▁Afg 53995 ▁لجنة 53995 ▁рівні 53994 ▁эшмәкәре 53994 ▁ಟೆಸ್ಟ್ 53994 şiktaş 53993 ര്യം 53993 ọdụ 53993 かれ 53993 ▁Samt 53992 ንብ 53991 ▁dejtings 53991 ▁खाना 53990 ▁adren 53989 ▁done 53989 ▁suurin 53989 ======== 53988 hik 53987 дзень 53987 ▁Gori 53987 ▁ଉତ୍ତର 53987 ▁olvid 53986 ▁મોટી 53986 ▁सत 53984 ▁찍 53984 ჭი 53983 ụng 53983 ▁vėliau 53983 ▁Хол 53982 ▁υποστη 53981 ਡਰ 53980 લ્પ 53980 詢 53980 ականի 53979 ▁ግል 53978 ▁obě 53977 ▁оформи 53977 enky 53976 ເບິ່ງທາງເຟສບຸກ 53976 ▁Haapt 53976 ▁länger 53976 nikiem 53975 cot 53974 ▁Elektro 53973 առնում 53972 ▁Diplom 53972 ▁hE 53972 ▁சென்று 53972 itaji 53971 ▁miliki 53971 ▁musu 53971 ώνες 53970 ▁leia 53970 ▁pozwoli 53970 ▁élèves 53970 ▁Centra 53969 ▁Obligator 53969 ității 53968 രിച്ചത് 53967 ▁1941. 53966 -400 53965 ▁slova 53965 ▁Mayu 53964 ▁yhteen 53964 ▁رضي 53964 ີ່ມ 53963 ▁prikla 53963 ▁علائقي 53963 ▁sols 53962 ▁为 53961 ▁consenti 53960 ▁유명 53960 ▁возра 53958 ग्री 53956 ្សែ 53955 ▁boobs 53954 առակ 53953 ▁rayu 53953 ▁спосіб 53952 ▁forh 53951 ▁አልተ 53951 φια 53948 ▁kaupa 53947 ▁חוב 53947 ▁krijg 53946 ▁landen 53946 ▁respektive 53946 zczegól 53945 ▁jirta 53944 ▁বিষয়ে 53943 hlaim 53941 влено 53941 वू 53940 हेब 53940 ▁personales 53940 ▁ສູງ 53940 ▁svakodnev 53939 ▁ធំ 53939 ▁confiance 53938 േഷം 53937 ▁նախաձ 53937 ▁सुपर 53937 進入 53937 amblea 53936 وبر 53936 ▁asup 53936 ▁ପହଞ୍ଚ 53935 ▁bangakwazi 53934 ▁liammet 53934 ▁Үндсэн 53934 ustri 53933 ▁ΓΙΑ 53933 ▁kå 53932 ▁חוז 53932 ▁მარტი 53932 ▁Krimin 53931 ▁제대로 53929 ▁beant 53927 خش 53925 ▁сегодняшний 53925 საქართველო 53924 キュ 53924 Berry 53923 ▁Bolog 53923 ogado 53922 ìm 53922 ▁151 53922 計算 53921 ନାହିଁ 53920 ▁gimnaz 53920 ▁kamata 53920 પોર્ટ 53918 monės 53915 ▁ketua 53915 फार 53914 ▁फल 53914 ይነት 53912 ▁организи 53912 ▁ресурси 53912 量的 53912 ึ้ง 53911 ője 53910 ▁Virus 53910 ▁בייַ 53909 אין 53907 ▁Głów 53907 の中に 53907 ▁Innen 53906 ▁съобщи 53906 nop 53905 ▁атрымлі 53904 àraich 53902 ئه 53901 ▁rø 53901 ▁κατασ 53901 ekret 53899 elerde 53899 нией 53898 മുണ്ട് 53898 ▁Mida 53898 ▁devin 53898 ▁นอกจากนี้ 53898 សំ 53897 ▁זכויות 53897 აშა 53896 ▁Breaking 53896 ▁szuk 53895 eleh 53894 ithí 53894 ▁efectiv 53894 arası 53893 ▁urbano 53893 ▁ҡатнашыусы 53892 ίνονται 53890 ▁prasid 53890 ▁teksten 53890 ▁સાહિત્ય 53890 fragen 53889 венту 53889 ▁Tommy 53889 ▁מעבר 53888 ▁bjo 53887 lanib 53886 joh 53885 ▁দিল 53885 ▁خشک 53883 קשה 53882 ▁հիմա 53882 ▁ಮಹಿಳೆಯ 53882 ▁Objav 53881 ▁Oif 53881 ▁imza 53881 ▁μελέ 53881 ശ്ച 53880 ▁Kullan 53880 ▁Einnig 53879 ▁tants 53879 ミング 53879 ẳng 53878 อเมริ 53877 ▁Fid 53877 ▁жүр 53877 മായും 53876 ▁otroke 53876 ▁κω 53876 ▁oferece 53875 ▁voorkom 53875 ▁Eno 53874 ▁Sammen 53874 ▁विश्वविद्यालय 53874 ▁pikë 53873 ▁sobretot 53873 ▁wethu 53872 ▁ереже 53872 ▁bhua 53871 ojne 53869 ▁auki 53869 ۰۰۰ 53868 ຈມ 53868 ▁želje 53867 ▁नवी 53867 ibiina 53866 มพู 53866 чков 53865 ▁müzik 53865 ▁ഉണ്ട് 53865 っていない 53865 ▁darīt 53864 ▁մասնակից 53864 abilità 53862 ▁безпе 53862 ▁ವಿದ 53862 wale 53861 shan 53860 ვნის 53858 ▁چهره 53858 ▁predlog 53857 ▁scoil 53857 šwe 53855 опа 53855 ▁publics 53855 ▁trưng 53855 ▁τουλάχιστον 53855 ▁الشمال 53855 ▁fotball 53854 ▁vackra 53854 ▁السوق 53854 ▁ผลิต 53854 ▁სტი 53854 ▁fahren 53853 ▁extens 53852 ▁tartalom 53852 ▁ସ୍ଥାନରେ 53852 ▁ਲੋੜ 53851 ▁kwesịrị 53850 ▁싸 53850 dage 53849 šou 53849 apia 53848 ▁රජයේ 53848 奉 53848 ▁ڇپ 53847 ainm 53846 ▁Ifølge 53846 ▁Latvia 53846 ▁엄마 53845 ▁Constan 53842 ▁vaadata 53842 ană 53840 ▁мазни 53840 ▁인천 53839 個人的 53839 ҡо 53838 ጠፋ 53838 ▁pudo 53838 ▁Одлу 53838 ▁försöka 53837 ▁tecnolo 53837 ▁visszat 53837 ▁වෙලාව 53837 ▁받을 53837 ▁Boq 53836 jea 53834 impunan 53833 ongoza 53833 ▁komentāru 53833 혹 53831 ▁capitala 53829 ▁senin 53829 ▁xilka 53829 Und 53828 ▁Waarom 53828 ▁КУ 53828 ▁folgende 53827 ▁ට් 53827 ▁aparecer 53826 ▁ponsel 53826 ▁यूपी 53826 Lux 53825 ▁სამსახ 53824 arke 53822 ▁વાર્તા 53822 kanje 53821 oriad 53821 ffs 53819 regen 53819 ▁केंद्रीय 53819 反应 53818 mobil 53817 ▁diber 53817 ▁شعار 53817 ▁2025 53816 ▁biznis 53816 ▁contamin 53816 ▁министрлігі 53815 ሎጂ 53814 ▁zasebnosti 53813 ▁പൊലീസ് 53813 ▁cev 53812 ▁ವರ್ಗ 53812 ▁සංස්ක 53812 ▁ସର 53811 部署 53811 ▁ewech 53810 ▁арҡаһында 53810 ▁перегово 53810 ▁paydo 53809 _-- 53807 ▁Anat 53807 ▁зогсо 53807 ▁கைது 53807 ▁कॉलेज 53806 ▁Hawa 53805 atymo 53804 вшего 53804 лууд 53804 ▁lyt 53804 ▁midler 53804 ▁տեղափոխ 53804 JET 53803 ▁Tyl 53803 ρεάν 53802 ▁karte 53802 ▁излиза 53802 ▁споме 53802 事は 53802 ătorii 53801 ▁PUN 53801 ▁incep 53801 ▁mł 53800 ▁клиенти 53800 ▁Behand 53799 alarida 53798 ▁alligevel 53797 ▁vairu 53797 ค้น 53796 ▁56. 53796 ▁برخور 53796 พง 53795 ▁оруулах 53795 ▁נער 53794 elingen 53793 ▁Cumhuriyet 53793 ▁mediji 53793 likooli 53792 ٹرول 53792 ▁pembaca 53792 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▁članka 53415 elut 53414 pade 53414 וניברסי 53414 を持つ 53414 ▁Бой 53412 ম্যান 53411 ලිබ 53411 ▁anong 53411 钟 53411 ▁kristi 53410 ▁аҫтында 53410 ▁Özellikle 53409 ▁মন 53409 ുമ്പോ 53408 ถูกต้อง 53408 ▁Aften 53408 ▁pakt 53408 ▁илдә 53407 ▁мұғалім 53407 ▁eroti 53406 ▁díl 53405 ▁kompy 53405 ▁реформи 53405 jach 53404 ▁Torino 53404 ▁Martín 53403 ▁продукция 53403 ▁anezhañ 53402 ▁самов 53401 披 53400 ORES 53399 ናገር 53399 ▁priek 53399 ▁stäm 53399 altungs 53398 ▁beses 53398 ▁ddau 53398 ▁nm 53398 ▁publique 53398 ▁ഭാഷ 53398 ▁спасылкі 53397 ▁atkal 53396 อีกครั้ง 53395 ▁tecniche 53395 ಬು 53393 ▁omuntu 53393 ▁يب 53393 ▁त्याचा 53392 ▁ሆነው 53392 ກົດຫມາຍ 53391 agibeli 53390 ▁actes 53390 Dem 53389 ▁apmēr 53389 ▁прошлом 53389 ▁Խաչ 53388 ▁милијар 53387 äss 53386 ▁formie 53385 ▁неста 53385 ▁சங்க 53385 illari 53384 الج 53384 маў 53383 ▁arbejdet 53382 ▁전시 53382 ▁خص 53380 ompok 53379 ນີ 53378 ▁небезпе 53377 kenalkan 53376 ▁Watu 53376 ▁kræver 53376 ▁parash 53376 ναντι 53375 ▁Rhag 53375 ▁haji 53375 ▁प्रका 53375 فيق 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53332 ्ना 53331 ▁saame 53331 ▁shan 53331 liğini 53330 ▁Поря 53330 ▁автомобиля 53330 ▁උත්සාහ 53330 ▁коллед 53329 ▁فك 53328 ▁পৌ 53328 ,( 53327 érés 53327 ▁HU 53327 ▁বয় 53327 ▁gemens 53326 ▁الدفاع 53325 rechtleche 53322 ▁luok 53322 λεγ 53321 ▁kuwum 53321 ▁ευχαρισ 53321 ▁књига 53321 ▁معاملات 53321 jda 53320 ຟ້າ 53320 ▁pasla 53320 ▁rhyng 53319 ▁Гол 53319 ▁3500 53318 ▁aprendiza 53318 ▁programmi 53317 公告 53316 ▁tampa 53315 ▁voler 53315 ▁класа 53315 ▁תהיה 53315 ▁ಕಾರು 53315 ▁ಮಾರ 53315 čiais 53314 ିକି 53314 ▁വിഷയ 53312 əlman 53311 onfur 53310 علنت 53309 ▁легендар 53309 ▁Ղարաբաղ 53309 ▁mengobati 53308 بعض 53306 ▁العالمي 53306 ▁تمرین 53306 ▁tompon 53305 kku 53304 נדל 53303 ▁území 53303 ิบ 53302 ▁anerem 53302 elej 53301 တိုင္ 53301 ▁4% 53301 ალს 53297 ▁привести 53296 ▁dostan 53295 ▁абра 53295 פּט 53293 ወስ 53293 ▁dedicata 53292 ▁Προσ 53292 zaileak 53291 ▁специалисты 53291 ▁ನಿರ್ಮಾಣ 53291 ▁رأس 53290 ACT 53289 urid 53289 ▁Уфим 53289 ▁reducir 53288 חב 53287 ▁الاجتماعية 53287 ▁distintes 53286 ▁በበ 53286 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elligence 53245 ▁zobaczyć 53245 такми 53244 рованы 53243 ▁ይሆና 53242 ▁اسٽي 53240 নিবার 53239 ▁Lös 53239 inies 53238 гає 53236 ▁Sheikh 53236 ▁Зоран 53236 ଲିକ 53235 ▁υπηρεσίες 53235 ▁هیڅ 53234 ▁refugi 53233 ▁иниција 53233 ▁فراموش 53233 ▁পূ 53233 ▁ଉଠ 53233 ▁ரி 53233 കേ 53232 ▁Dagbladet 53232 ▁karak 53232 ▁pintar 53232 ▁चर 53232 ▁ໂລກ 53232 erez 53230 мед 53230 ▁consigu 53230 ▁ønsket 53230 ▁торгов 53229 ▁جذاب 53228 ▁rôle 53227 ▁विकि 53227 ськими 53226 ▁högre 53226 ▁Fotoen 53224 ▁muš 53224 ໍານົດ 53223 ▁intégr 53222 ▁sebaiknya 53222 ▁zanak 53222 ▁ਚੋਣਾਂ 53220 ▁lakaet 53218 ▁pierdut 53218 ▁ମିଛ 53218 HK 53217 ▁janeiro 53217 ▁тапсы 53217 ▁buang 53215 ▁پشاور 53215 ķis 53214 паѓа 53214 လှ 53214 ▁madi 53214 ▁реалізації 53214 ▁جمه 53214 ▁ciut 53213 ▁gysyll 53213 ▁Halb 53212 ▁hẳn 53212 ▁taux 53212 ▁znanosti 53211 ▁āt 53211 ▁ኅ 53211 ▁nějaké 53210 ▁valam 53210 ▁pump 53209 ئاسة 53208 ડિયો 53208 ▁Maine 53207 keeda 53206 àtica 53204 ▁нээлт 53204 ▁എന്നാണ് 53204 loka 53203 ▁доколку 53203 ikku 53202 ▁piac 53202 ▁qualques 53200 ▁болоод 53199 ▁produsele 53197 ိုးရ 53196 ▁participan 53196 غذ 53195 ▁ڈا 53195 μάς 53194 ▁telefone 53194 莉 53194 ▁alrededor 53193 ▁működés 53193 istų 53192 ▁njeg 53191 ▁தயா 53191 istrat 53190 երս 53190 իկա 53189 อรี่ 53189 ▁ritual 53188 rúar 53187 ▁Marine 53187 ▁trwa 53187 気づ 53187 ▁пароль 53186 ▁अजून 53186 ਅਮ 53185 ▁príspevkov 53185 ▁дипломати 53185 ficial 53184 시고 53184 ҙарҙың 53183 ▁vườn 53183 ▁Até 53182 spil 53181 ▁زره 53181 леріне 53180 ადგენს 53180 ▁двор 53180 ▁Agency 53179 ▁denkt 53179 ▁الرحمن 53179 ▁apho 53178 ▁mütləqdir 53177 ▁வாய்ப்பு 53177 ▁pronunci 53176 andez 53175 陰 53175 ščina 53174 ▁punktu 53174 ▁wolle 53174 融合 53173 змот 53172 حتى 53172 ▁Mold 53172 czna 53171 ▁balita 53171 ▁аху 53171 ▁киләсәк 53171 ▁конфи 53171 ▁dokáže 53170 ▁کولای 53170 ▁유럽 53169 नाम 53167 ▁Aquela 53167 ▁өрт 53167 ecan 53165 ણું 53165 ▁kaq 53164 ▁дазва 53164 ▁Këto 53163 elmän 53161 kemmañ 53161 köping 53161 ▁Antwort 53161 ▁katutubong 53161 ▁גוף 53160 мит 53159 尤其 53159 əsilə 53158 닐 53158 ▁mfano 53157 јев 53155 ▁KUP 53155 ▁Seán 53154 ▁хүрт 53154 ▁କାହିଁକି 53154 הא 53153 დერ 53153 ▁якій 53153 ▁نوب 53153 ▁Biznes 53152 ▁Hela 53152 fuld 53151 ▁tänä 53151 ▁ያሉት 53151 เศ 53149 ▁ontstaan 53149 ▁жаңалықтар 53149 ▁электр 53149 ಬೆ 53148 ▁prowadzi 53148 ▁нощ 53147 ▁ඔවුන්ගේ 53147 ▁isig 53146 ▁stål 53146 واق 53145 ▁dež 53145 ▁prox 53145 ▁schn 53144 ▁ипоте 53144 ▁लौ 53144 ଋ 53143 δειγμα 53142 сього 53142 לוי 53141 ▁заболевания 53140 ▁گام 53137 nungs 53135 ▁bitartean 53135 ▁erfarenhet 53135 ▁odpowiada 53135 ▁טאקע 53134 ▁nél 53133 esaki 53132 ▁Sankt 53132 ▁duka 53132 ukia 53130 ราบ 53129 ▁тайл 53128 ▁Մեծ 53128 ▁نارو 53128 abilidad 53126 ▁wem 53126 ▁mesura 53125 ▁принимать 53125 fting 53124 uah 53124 зві 53124 igambo 53123 ล้าน 53123 ▁zostało 53123 færi 53122 intir 53122 ▁dzies 53122 ▁സ്ഥാന 53122 ▁verkefni 53121 ▁внед 53121 atangan 53119 wá 53119 ▁Порту 53119 ▁معرض 53119 ▁第二 53119 եջ 53118 ▁μπορούσε 53118 ▁студентів 53118 ▁බලය 53118 jew 53117 ▁Fish 53117 ▁dłu 53117 ▁reprezintă 53117 ▁ელექტ 53117 ▁diben 53116 følgende 53115 כא 53114 raction 53113 ▁первом 53113 ▁פּאַ 53113 ▁Aydın 53112 ▁possibilité 53112 ▁ඉල්ලා 53112 ▁อุ 53112 ▁케 53112 ▁stemme 53111 enteen 53110 Ге 53110 ▁νομο 53109 ▁ಜನ್ಮ 53109 也就 53109 ohja 53108 ▁podrà 53108 ▁თქვენს 53108 ▁Böyük 53107 ▁məz 53107 ▁netur 53106 ▁চা 53106 engozi 53105 ▁नक्की 53105 empresa 53104 urasının 53104 ጠሩ 53104 そも 53104 ഷണ 53103 ▁dhawaa 53103 ชุมชน 53102 ▁Μακεδον 53102 barzh 53101 ▁некото 53101 ▁периоди 53101 ▁Zuid 53100 學校 53100 ам 53099 ▁ସ୍ମାର୍ଟ 53099 bios 53098 ▁patīk 53098 หยัด 53097 ▁mérték 53097 恐怖 53097 ▁সির 53096 cisi 53095 wòl 53095 ▁Double 53095 ▁hutan 53095 ▁Кро 53095 ▁ڪڍ 53095 ▁Ballkan 53093 ▁Տար 53093 ▁месцы 53092 ▁мужчина 53092 မရ 53091 ▁aromat 53091 ▁ପ୍ରତିଶ 53091 ▁kulut 53090 ▁nakonec 53090 ಮಾರ್ 53088 ▁səf 53088 ▁pericol 53087 yfyrwyr 53086 ▁raccont 53086 ▁دفا 53086 АС 53085 дната 53084 ▁biler 53084 ▁vlieg 53084 ▁amince 53083 ▁авна 53082 ▁камер 53082 ▁تنهنجي 53082 ▁मिडिया 53081 vällen 53080 ▁umiejętności 53080 ▁संसार 53079 ▁angel 53078 ▁kaits 53078 ▁ຊ່ວຍ 53076 ሔር 53075 ▁المياه 53075 dyn 53074 isterö 53074 ▁moest 53073 ▁مود 53073 ţile 53072 ากฏ 53072 បោះ 53072 ▁AX 53072 ▁obrav 53071 ▁wersji 53071 ionit 53069 бено 53068 ଙ୍କା 53067 ደግ 53067 ▁trh 53066 ▁อุปกรณ์ 53066 онд 53065 ▁tạp 53065 क्रिय 53064 ▁απαραί 53064 ▁Днес 53064 ၿမိဳ႕နယ္ 53063 layıb 53062 ▁yleis 53062 ▁байхад 53062 ▁предпри 53062 ▁Балкан 53061 が生 53060 ▁хэмжээг 53059 ເປ 53058 扩 53058 otājs 53057 ▁activitatea 53057 ▁tindak 53057 ▁கொடுத்த 53057 adament 53056 ▁altele 53055 ▁تھیں 53055 ▁zion 53054 ▁չկա 53054 ▁ଜାରି 53054 ▁aspetti 53053 ▁skape 53053 ▁הדברים 53053 Health 53052 लेख 53052 ▁biser 53052 ▁dokonal 53052 モデ 53052 OSTI 53051 ophi 53051 ▁cheve 53051 ▁المسي 53051 ▁Ús 53050 ▁பள்ள 53050 歷史 53050 γιο 53049 ▁répond 53049 deri 53048 ▁медиа 53048 ▁නොමැති 53047 сити 53045 زال 53045 ▁recerca 53045 ogus 53044 ▁plāno 53044 ▁Дү 53044 具备 53043 ▁самолет 53040 uldig 53039 ustil 53039 藝 53039 科研 53038 ▁reclam 53037 ▁Näit 53036 ▁fyrirtæki 53034 もあり 53034 陣 53034 ▁latha 53033 ▁기념 53033 ijet 53032 rwydd 53032 въз 53031 ನೀ 53031 isita 53030 ▁krim 53030 ▁таңда 53030 그램 53030 ▁εμείς 53029 wakha 53028 ▁Германија 53028 ▁கல்ல 53028 વાલ 53027 ▁município 53026 悩み 53024 эхгүй 53023 ▁דריי 53022 ▁smislu 53021 ▁ترلاسه 53021 λική 53020 baş 53019 elten 53019 тък 53019 انیا 53019 පෑ 53019 ▁çalışır 53018 ▁wens 53017 ▁bezpieczeństwa 53016 ませんか 53016 чници 53014 ▁Gesundheit 53014 ▁البري 53014 ▁nare 53013 ▁מוצרים 53013 ▁përm 53012 ▁цэнтра 53010 くい 53010 ▁گھن 53007 =5 53006 ▁midis 53006 ρήσει 53004 ▁eziqondile 53004 。。。 53004 iniuose 53003 ▁nòstre 53003 ▁الإمارات 53003 ▁utmär 53002 ▁նպատակով 53002 ▁Wunn 53001 ▁[6] 53001 ▁uskuto 53000 ▁Lọ 52999 diga 52998 ▁مسئولان 52998 iqtis 52997 ▁Mozilla 52997 ಕ್ಕು 52996 ▁berkualitas 52996 ▁gemakkel 52996 ▁resultaten 52996 ▁തൃ 52996 iteits 52995 ▁ukukhokha 52995 ▁užsak 52995 ▁계약 52995 ಮಾರಸ್ವಾಮಿ 52994 ▁απόλυ 52994 جيه 52993 egeben 52992 اسې 52992 ्याल 52992 ▁γλυ 52992 ▁ಸಂಭ 52992 ▁ewéi 52991 сыһы 52990 ocos 52989 ▁rudaí 52988 ▁Украі 52988 artay 52986 ▁husto 52984 ▁khúc 52984 ▁γραφ 52984 ាណិជ្ជ 52983 ▁prácticas 52983 ▁вд 52983 ▁terbukti 52982 ▁항상 52982 εία 52981 ▁ساعة 52981 ګو 52977 ▁janúar 52977 ▁psich 52977 △ 52977 čním 52976 ளிய 52976 ໄວ 52976 ▁knyg 52976 ▁ngato 52976 ▁լուսանկար 52976 ▁만큼 52976 ▁Ily 52975 ▁Proses 52975 ▁bordo 52975 ▁Belajar 52974 ▁Uporab 52974 ▁alka 52974 ▁تنظ 52974 ▁temporal 52973 ▁umsagnir 52971 ▁машини 52971 ▁माझी 52971 ▁esetleg 52969 ▁sobě 52969 ▁siempres 52968 ▁වර්ග 52968 ▁ezithinta 52967 ▁tris 52967 արկում 52966 ▁Eduk 52966 ▁MOKAPO 52965 ▁vostre 52965 ▁kome 52964 ▁животни 52964 템 52964 ▁Ea 52961 ▁ایده 52961 ამოს 52960 ▁inner 52960 ▁увре 52960 :” 52959 策略 52959 ▁Vân 52958 ▁સ્ત્રી 52958 ワード 52956 ▁schade 52955 ▁подготовки 52955 拔 52955 ψαν 52954 зий 52954 ▁купа 52954 cijā 52953 ▁בשביל 52953 ▁لاڳ 52953 ▁पल 52953 ▁8: 52952 ▁подготовка 52952 bourg 52951 යෙන්ම 52951 ▁منظم 52951 kreis 52950 สาธาร 52950 ▁tegas 52950 וספת 52949 ვედ 52949 ើញ 52949 ▁niekedy 52948 かもしれませんが 52948 那里 52948 으나 52948 ▁ɗiɗi 52947 ▁മനസ്സിലാ 52947 ▁ضبط 52946 fax 52945 ▁světě 52945 ▁kathah 52944 ▁kaunis 52944 ▁lehilahy 52944 运输 52943 ▁Fet 52942 ▁oluşan 52942 ▁qaatay 52942 ▁Nový 52941 ▁Мэдээл 52939 ▁соңғы 52938 審査 52937 ल्याने 52936 ▁sadarkaa 52936 ▁реб 52936 ماية 52934 ▁Cuộc 52934 ▁seharusnya 52934 ▁anaty 52933 ▁voldo 52933 ▁الذى 52933 ▁முடியும் 52933 kona 52932 ▁Informazio 52932 ▁ממנו 52932 ▁پوشش 52932 ▁पत्रिका 52932 ▁මෙසේ 52932 ▁Monaten 52931 ▁nurod 52930 ▁Izra 52928 ▁maganda 52927 ▁εποχή 52927 ▁nút 52926 ▁resul 52925 ▁selvom 52924 ajate 52923 kontrol 52922 ▁Kennedy 52922 ▁والاست 52922 kää 52921 ▁තැන් 52921 ាសន៍ 52920 лізації 52919 ▁വിശദ 52919 opor 52917 ▁الشي 52917 ครง 52916 ▁Flücht 52916 ▁korábbi 52916 ▁parkov 52915 性和 52914 ▁Divertissement 52913 ▁гек 52913 ▁новую 52913 ▁Bukid 52911 ▁delovanje 52911 ▁بالس 52911 ▁ചിന്ത 52911 ▁അവന് 52910 ▁предыду 52909 ▁হার্ডকোর 52909 isigud 52906 بته 52906 ▁priliku 52906 ěn 52905 ويه 52905 ▁Rann 52905 ▁үнэн 52905 ▁मनोर 52905 чот 52902 ▁Ghana 52902 ίστα 52900 ξηση 52900 ▁klabu 52900 edne 52899 тээ 52899 ▁prys 52898 ▁gceist 52897 ▁mokolo 52897 ▁nosas 52897 ▁restric 52897 ▁نبا 52896 ուք 52895 ▁✓ 52895 ▁stiah 52894 ībās 52893 ▁Νε 52893 ਚੀ 52891 ್ವ 52891 ▁عالمي 52891 ಸಂ 52890 ▁scheint 52890 ▁클릭 52889 ▁adil 52888 ▁آتی 52887 ▁Mọi 52886 ▁സുഹൃ 52886 hadap 52885 ▁ધરા 52885 ▁Adama 52884 ▁کھلاڑی 52883 angers 52882 entia 52882 ênêng 52882 რილი 52882 ▁Masyarakat 52882 ▁Ungase 52881 ▁kleiner 52880 ▁دوشنبه 52880 ▁पुगेको 52880 ▁internal 52879 ▁rret 52879 ▁тэрыторыі 52879 ▁कपूर 52879 ▁minne 52878 ▁részére 52878 halts 52877 ▁သင္ 52876 leja 52875 जाल 52875 ▁ultimele 52875 indel 52873 ▁pakkum 52873 ▁dosis 52872 ▁fortuna 52872 ▁دهیم 52872 ଳକୁ 52871 ▁közötti 52871 ▁බං 52870 ▁istak 52869 ▁подруч 52869 ▁վարկ 52869 říve 52868 ρίς 52868 ▁ια 52863 ▁এটা 52863 ▁टाई 52862 anaj 52861 nuté 52861 ▁ອຸ 52861 ▁besonh 52860 ▁ssente 52859 okolade 52858 moz 52857 ▁kambarys 52857 ಚಿತ್ರ 52855 的影响 52855 obox 52854 ็ง 52853 ▁entusias 52853 ▁veb 52853 டல் 52852 ನಾಥ 52852 สมบัติ 52852 ▁Drej 52852 ▁Europese 52852 ▁เผ 52852 ▁individuelle 52849 ▁بگیرید 52849 ▁ligesom 52847 ▁४० 52846 ▁جسٽس 52845 لاك 52844 ▁Крим 52844 ▁Ljub 52843 ▁akkuma 52843 ▁Հարություն 52843 ▁Müz 52842 ▁Україну 52842 ▁gehien 52841 ▁ஆன் 52841 ▁neil 52840 ▁očekuje 52839 hitra 52838 ▁Fios 52838 ▁shkruan 52838 ▁스타일 52838 ▁sistèm 52837 ▁дължи 52837 няў 52836 різня 52835 ▁تیز 52835 ご利用 52835 කුණු 52834 ▁Dobro 52833 ▁проектҡа 52832 ▁cantante 52831 ▁Bestellung 52830 ▁entidade 52830 ▁neemt 52830 tegi 52829 ▁faham 52829 ▁ouders 52829 ▁personaxes 52829 ▁psy 52827 ▁последњи 52827 ▁vyj 52825 geng 52824 ▁3/4 52823 ugarren 52822 ▁haast 52822 ▁notiks 52822 ▁tháinig 52822 ▁wellness 52822 ▁події 52822 ▁இள 52822 ▁rivol 52821 ▁айма 52821 ▁Bratislave 52820 ▁가능합니다 52820 共产 52820 ecem 52819 ▁hrvatske 52819 ▁nửa 52819 ▁ගෑ 52819 ▁금융 52819 ્યાર 52818 ▁تصوی 52817 шком 52816 ▁mövz 52816 ▁عورتن 52816 meeschter 52815 િયન 52815 Heb 52813 Card 52812 ciz 52812 ▁Zanim 52812 ▁ajaran 52812 upán 52811 رافیک 52811 ▁זיינע 52809 ▁végzett 52808 ሳን 52807 ▁адамдар 52807 ▁Wonder 52806 ປະສ 52805 דזש 52804 ▁ابزار 52804 ningum 52803 ▁altos 52802 anei 52801 ▁ٻيا 52801 ▁raid 52800 ▁roj 52800 ▁विश्वकप 52800 ▁Hóa 52799 gala 52798 ▁מאפשר 52798 ▁байдлыг 52796 efana 52794 θύν 52794 ยุค 52794 ▁κρίση 52794 ▁Σχ 52793 ▁legit 52791 ▁mõist 52789 спекти 52788 ▁razm 52788 ▁τί 52788 ▁बिक्री 52788 ▁огла 52787 ishli 52786 ▁ανάμεσα 52786 ▁والدین 52786 кес 52785 ▁bebek 52784 燥 52784 bourne 52783 ▁bır 52783 ▁ymg 52781 বেদ 52780 ▁Pry 52780 ▁stads 52780 ▁Kni 52779 ▁korrek 52779 ▁Estáu 52778 ▁sult 52778 ▁ધર્મ 52777 ▁ಮಟ್ಟ 52777 дих 52776 ▁Paran 52776 .25 52775 ▁նկատ 52775 ▁Ungakwazi 52774 ▁વ્ય 52773 שיבות 52772 pertatge 52771 ▁frumoase 52771 ▁nagusia 52771 建議 52771 ▁talento 52770 ▁oversk 52769 ▁хад 52769 IZI 52768 ଥିତି 52768 ಾಯಿಸಿ 52768 ▁ugovor 52768 ▁ਦੋਸ਼ 52768 ▁Vasta 52767 ▁کاغذ 52766 ▁uczniów 52765 րիստ 52764 ▁hatalmas 52764 ▁nessuno 52764 ▁Domen 52763 віў 52762 ▁Dempués 52762 ▁ဂ် 52762 ▁HIN 52761 给你 52761 dong 52759 ▁dawlad 52759 ▁entregar 52759 ▁βιβλίο 52759 esiNgesi 52758 ▁Sputnik 52757 만남 52756 ológiai 52755 ថយន្ត 52755 ▁ಅನ್ನ 52754 ሰሩ 52753 ▁Quest 52753 ▁फाइ 52752 ▁ሊቀ 52752 वर्ष 52751 qid 52749 ▁தான 52749 ▁ದೇಶದ 52748 νουν 52747 வதற்கு 52747 ѣ 52743 ▁Mistral 52742 ▁енгле 52742 ▁eede 52741 ▁الجسم 52741 yida 52740 λετε 52740 ▁ڈالر 52739 волі 52738 ▁Gate 52737 ▁Source 52737 表演 52737 ▁organise 52736 ▁terri 52736 ▁wiene 52735 ▁අමතක 52735 ujar 52734 цай 52734 ▁Глав 52734 ▁napja 52733 ▁Nhận 52732 ▁vəsait 52732 を取 52732 koje 52731 ūn 52731 ▁portat 52731 ographie 52730 ▁huono 52730 ካባቢ 52728 ▁reto 52728 ▁skik 52728 uller 52727 ▁corn 52727 ▁försöker 52727 จัดส่ง 52726 ▁erish 52726 ▁макар 52726 liklə 52725 ▁ultimul 52725 译 52725 ▁ग्रामीण 52724 ព្រឹ 52723 ▁Nationale 52723 ▁taasisi 52723 ▁وغيرها 52723 ▁አንዳንድ 52723 缘 52723 ▁rozwiązania 52722 ▁сё 52721 ▁ترام 52721 ▁مفا 52721 ▁Sāk 52720 alltainn 52719 ▁Gb 52719 ▁Клю 52719 ▁EUA 52718 Para 52717 ▁Avenue 52717 ediad 52716 ▁महत्त्वा 52716 工资 52716 fäl 52715 ▁mengirim 52714 ▁penderita 52714 Вы 52713 ▁Hanna 52713 ▁Samuti 52713 ▁genuch 52713 ▁przyzna 52713 eky 52712 ▁fëmijët 52711 osaigh 52710 óga 52709 сөлд 52708 പടി 52708 ▁dechrau 52708 ▁ಮಾಜಿ 52708 ▁ဤ 52708 ▁dedica 52706 ▁Կապ 52706 ēc 52705 ▁vhodný 52705 ojama 52704 ສາຫະ 52704 ▁+7 52704 ▁qoftë 52704 ▁trou 52704 ▁ملګرو 52704 ▁traff 52703 ▁තිබ්බ 52703 ▁TAG 52702 ▁קורס 52702 ▁ඝා 52702 ▁បង្កើត 52702 ▁Zn 52701 čnem 52700 กว้าง 52700 ▁belki 52700 initi 52699 ▁کيږي 52699 ▁Angst 52698 vnte 52697 ▁ਪਾਕਿਸਤਾਨ 52697 ▁Gonz 52696 ▁Aam 52695 ▁chậm 52695 ▁Doe 52694 ▁articolo 52694 γον 52693 ▁મેને 52693 কাপ 52692 ▁addu 52692 aiak 52691 ▁Ärip 52691 ▁200.000 52690 veilig 52689 ▁ευκαιρία 52689 ▁цэвэр 52688 ▁arrin 52687 ▁asr 52687 СТАН 52686 ▁Kingdom 52686 ▁திர 52686 ▁ANYỊ 52685 ▁पाहिजे 52685 ▁визначення 52682 ▁areng 52681 要素 52681 вершен 52680 ▁Midt 52680 ▁verstehen 52679 ▁февра 52679 ▁שווער 52679 ▁economi 52678 ektet 52677 ▁nekom 52677 ▁laaste 52676 斯坦 52675 adaama 52674 ▁haluat 52674 素敵 52674 ومتی 52673 ▁forældre 52673 宝宝 52673 ▁espesyal 52672 ▁uansett 52672 tryck 52671 ▁03, 52671 ▁viaţa 52671 ल्न 52670 ▁elf 52670 ▁materiałów 52670 มหาวิทยาลัย 52669 ▁ответственности 52668 ▁וויסן 52668 хват 52667 ագան 52667 ▁২৫ 52667 ▁κιν 52665 ▁نامزد 52665 ▁פער 52663 ▁тауыш 52662 ▁ନିକଟରେ 52662 ▁៩ 52661 ▁맥 52661 州市 52661 ଦାନ 52660 ▁ဖြ 52660 राष्ट्रीय 52659 ieška 52657 ženja 52657 ▁Ove 52657 ▁Thánh 52657 प्ताह 52656 ▁felül 52656 ▁сүлжээ 52654 ▁കൊച്ചി 52654 ဲြ 52652 ▁FK 52652 ▁عاش 52651 ▁บทความ 52650 hoid 52649 ština 52649 ឿ 52649 ▁нашиот 52649 ▁Mim 52648 ▁cír 52648 ૂલ 52647 ▁Omar 52647 gne 52646 太多 52646 acağını 52645 ▁kantved 52645 ▁organizaciones 52645 ▁percent 52645 ▁dran 52644 ▁افسوس 52644 ▁Watanzania 52643 ▁meteen 52643 ▁қар 52643 otres 52642 ▁વ્યવ 52642 ினால் 52641 ▁4-6 52641 ▁zice 52641 ▁Ιουνίου 52641 ▁elnök 52640 ▁গুরু 52639 出す 52639 ▁බුදු 52637 ▁gesäit 52636 feitura 52635 ▁ஜெ 52635 ▁inš 52634 ▁puikiai 52634 partiet 52633 uliañ 52632 ▁perkataan 52632 đi 52631 ▁спы 52631 ▁вру 52630 сақ 52628 ▁ככל 52628 ▁നാട്ട 52628 หลังจาก 52627 ▁Ihan 52627 ▁поясни 52624 ▁युवक 52624 ▁Datos 52623 ▁Zig 52623 ▁ìta 52623 ▁συνεργασία 52623 ətə 52622 ▁betekent 52621 ▁фонды 52618 ත්තා 52617 ▁Artur 52617 ▁Etiketter 52616 ൈന 52615 ೀನ 52613 ▁Тие 52613 ▁አመት 52613 ▁meðan 52612 ▁orok 52612 ▁приятел 52612 ▁الماء 52610 atian 52609 gever 52609 сол 52607 ▁تکمیل 52607 ▁കല് 52607 ▁espacios 52606 ▁аднаго 52605 adení 52604 ▁prijave 52604 ▁întreb 52604 ▁интернете 52604 viac 52603 өс 52602 ▁រ៉ 52602 bilt 52601 ▁panggep 52600 ▁umf 52600 ▁qadar 52599 ▁आमा 52599 ▁विद्यार्थ 52599 ərkən 52598 ▁sözü 52598 ▁يعد 52598 ләндер 52597 ủy 52597 ▁conced 52597 ▁entitats 52597 ૂત 52596 ▁ιδιο 52596 新た 52595 缺乏 52595 slas 52593 ▁considerado 52592 enné 52591 ▁دعم 52589 ล้อม 52588 ▁bambino 52588 ▁krijuar 52587 ▁Оно 52587 ▁økonomi 52586 ▁ដុល្លារ 52586 estan 52585 ▁Meur 52585 ▁fotografía 52585 ▁sorprend 52585 ▁انگل 52584 antaranya 52583 ▁Sofa 52583 ▁chá 52581 LES 52580 ▁ennak 52580 ▁makam 52580 ▁muddat 52578 ବତ 52576 ▁нуди 52576 ▁Jut 52575 ▁jete 52574 ▁македонскиот 52574 ertal 52573 ▁apstr 52573 ▁absoluta 52572 ▁பக்க 52572 ಡಾ 52570 ▁Zuz 52570 akuan 52569 ▁Bapa 52569 صبح 52568 ▁nợ 52568 ▁medici 52567 펜 52567 ▁SUR 52566 ▁ác 52566 ▁ευρωπαϊ 52566 ungumzo 52565 ▁mengand 52565 ▁ມານ 52565 ▁helikopter 52564 ▁khiển 52564 ▁Νοεμβρίου 52564 atsen 52563 ▁Mặt 52562 ፖር 52561 ▁Aidha 52561 ▁အစိုးရ 52561 ▁axudas 52560 ▁المنزل 52560 Stre 52559 ▁sacens 52558 ▁béné 52557 ▁miljoni 52557 ▁namme 52557 ▁एकदा 52557 .[4 52556 макра 52555 ▁kituo 52552 ▁мое 52552 ıza 52550 ▁putu 52550 gga 52549 ▁кризи 52548 ▁пові 52548 itans 52547 ▁Jahrhund 52547 ိဳက္ 52546 ▁ponuja 52546 ▁हेर् 52546 ▁dispu 52545 ▁וְ 52542 astie 52540 ▁ජීවිතය 52540 ▁ڳالهيون 52539 ▁იან 52539 不想 52539 desmit 52538 ▁watch 52538 ▁משפט 52537 ▁민주 52537 랐 52537 ▁erga 52534 alub 52531 ▁kartaa 52531 ▁kaupung 52531 ▁ਹਾਦ 52531 ▁zwy 52530 izos 52527 ższy 52527 ▁ಅಂ 52527 ություններից 52525 ುವಂತೆ 52525 ▁kito 52525 ▁Diff 52523 աքս 52522 ದುಕ 52522 分かり 52522 tojë 52520 ▁probablament 52520 ▁ରି 52520 ▁stick 52519 ▁multif 52518 ▁기다 52518 крамя 52517 ansand 52515 ▁uchumi 52515 ▁въпреки 52515 ସ୍ୟ 52512 ▁Dene 52512 棄 52512 ΕΙΣ 52511 Ақ 52510 industr 52509 ▁Ceann 52509 ▁SMP 52509 ▁לאורך 52509 aliga 52507 overe 52507 wnątrz 52507 ಲುವ 52507 ประวัติ 52507 ▁chionn 52507 ▁කර් 52507 ▁mutlaka 52506 ▁расти 52506 ґ 52505 ▁concess 52505 ▁провин 52505 टै 52504 առավար 52503 omai 52501 ավայր 52501 ▁ראָ 52501 ▁firem 52500 ▁ΕΕ 52500 ▁Ters 52499 ▁püs 52498 ▁امير 52498 ▁කතාවක් 52495 ▁멀 52495 ▁Kamb 52493 ▁enlace 52493 ▁nágr 52489 eluun 52487 詳しく 52487 carea 52485 ▁സമാ 52485 əmməd 52483 ▁ਕੈਨੇ 52483 ▁Čia 52482 国外 52482 ▁2,000 52481 ▁addysg 52481 ▁nemusí 52481 *--- 52480 ▁rezon 52480 ▁ανεξ 52480 ▁qold 52479 जाब 52478 likka 52477 ▁Lenk 52477 ▁नियमित 52477 ▁visiems 52476 entei 52475 ક્રમ 52474 ▁ثقافت 52473 ▁প্রতিনি 52473 ▁Brno 52471 prowad 52470 ▁Igen 52470 ▁전략 52470 ▁Manfaat 52469 ▁santu 52469 ▁ਜਾਂਦੇ 52469 ▁တင္ 52469 ことがある 52469 ായത് 52468 ▁горада 52467 虫 52467 ▁xương 52466 าศาสตร์ 52465 ▁Հայաստանից 52465 消費者 52464 aquestes 52463 ส่งเสริม 52463 ▁aufgrund 52463 ▁воспользова 52463 ▁арасындағы 52461 ▁පහර 52461 ▁пакі 52459 ពិត 52458 ▁Kulit 52458 ▁taký 52458 ▁ஒவ்வ 52458 ▁구글 52457 さに 52457 сий 52456 ▁siasa 52456 ▁ವಾರ್ 52456 eluar 52455 νιο 52455 irid 52453 ய்ய 52453 ▁εάν 52453 ▁электри 52453 ▁ઘણી 52453 이버 52453 μην 52452 ▁δα 52451 drav 52450 Монгол 52450 ▁اکا 52450 妈 52450 jiin 52449 acjach 52448 եմատ 52448 ▁pitanju 52448 setning 52447 ▁träffa 52447 ▁прашања 52447 ▁жоспары 52446 did 52445 ▁רשת 52445 ihana 52444 ஹா 52444 ખ્યા 52442 ▁frak 52442 ▁Κωνσταν 52442 हेका 52440 ▁imagine 52440 ▁lundi 52440 ▁tydelig 52439 ▁adını 52436 ▁termes 52436 ጅት 52434 ▁fönster 52434 ▁புக 52433 ▁ነጥ 52433 átum 52432 ▁Andal 52432 ▁noticias 52432 ▁ПЕ 52432 ▁Interior 52431 ıllı 52430 ▁maelezo 52430 สํานักงาน 52429 ▁বো 52428 agarri 52427 ιστικό 52426 ▁tähelepan 52426 โฮ 52425 ▁taya 52425 收益 52425 ▁टै 52424 ▁Level 52423 ützt 52420 ▁geriau 52420 ▁гадаад 52420 irne 52418 ▁piata 52418 ▁Йы 52418 ▁محنت 52418 न्ही 52417 ▁سبق 52416 лима 52415 ▁orz 52415 ▁بذلك 52415 ▁நிலையில் 52415 ▁თამ 52415 ▁ჩატ 52415 kläder 52413 ▁لوړ 52413 ▁Tell 52411 ▁eGoli 52411 ▁experienta 52411 ▁kenne 52411 ▁quién 52411 ▁고려 52411 ▁Doar 52409 ▁Posi 52409 ▁چئي 52409 ÁM 52406 രത്തില് 52406 传播 52406 kosť 52405 dafiə 52403 odaw 52401 ▁лого 52401 ▁honey 52400 ▁spät 52400 ínház 52399 ▁нэгд 52398 ▁ਮੁੱ 52398 ▁hjelper 52397 ▁equipa 52396 ▁किशोर 52396 ▁गिरफ्तार 52396 ▁ஸ் 52396 难以 52395 ▁aiemmin 52394 ▁قبلی 52394 ▁Ugy 52392 ψηφ 52390 ▁ebet 52389 ▁försök 52389 ▁anglisy 52388 ▁Ajuntament 52387 ▁Dersom 52387 ▁deniz 52387 ▁esip 52387 ▁pogo 52386 ▁डिस 52384 तंत्र 52382 ▁Memp 52381 UMI 52380 foglal 52380 ▁gået 52380 ▁κόσμου 52380 prett 52379 proq 52379 拥 52379 殖 52379 diyy 52378 ▁hakkas 52378 ▁कुठ 52378 有多 52378 strong 52377 บอกว่า 52377 ប្រតិ 52376 ▁réguli 52376 ▁tangtu 52375 ▁រួច 52375 ▁kenbe 52374 ▁شوق 52374 を開 52372 ▁Naša 52371 ▁nyheder 52371 ließen 52369 āb 52369 ▁Düny 52369 ▁коллектив 52369 ▁հրաժար 52369 ▁લિ 52369 ▁degaset 52366 ▁warta 52366 ▁حامل 52366 ▁مقبول 52364 ▁үеэр 52363 ಾಚ 52362 ▁ljude 52362 ▁βρίσκονται 52362 ▁प्रशा 52362 ▁ļauj 52361 ▁кожны 52361 ▁430 52360 ▁شای 52359 íně 52358 ▁bynnag 52356 ▁reais 52356 ▁ọk 52355 ▁기반 52355 ▁consecuen 52354 ▁גוגל 52354 гет 52353 ନ୍ଦୁ 52353 ▁இருந்தது 52353 ▁fudhat 52352 idhm 52351 indano 52351 ▁vecinos 52351 ▁עולה 52351 ▁これは 52350 ▁مام 52349 ihar 52347 ▁জুন 52347 Eesti 52346 ▁ເວົ້າ 52346 ▁цвят 52345 igii 52343 raedd 52343 ▁örgüt 52343 ▁presto 52342 ▁ሰነ 52341 ▁zajm 52340 reten 52338 ▁գալիս 52336 ▁योगी 52336 εντ 52334 ▁අනේ 52334 ▁Deal 52333 ▁olo 52333 Hy 52331 ▁ផ្ស 52331 ▁difficoltà 52329 uca 52327 កូន 52327 isos 52326 ▁ଘୋଷଣା 52326 हात 52325 ▁Fáilte 52325 ▁Jeh 52325 南京 52325 ოზ 52323 ▁Potrivit 52322 ▁сведо 52321 פורים 52320 ▁DNS 52319 ▁entdeckt 52319 प्ति 52318 ረድ 52318 ▁Publicat 52318 ▁europeo 52317 21. 52314 ▁2010- 52313 álnu 52312 ង្ហាញ 52312 ▁معاہد 52312 ▁dega 52311 ▁prihvat 52311 ▁ማስተ 52311 ▁சாப்பி 52310 胃 52310 īgais 52309 ▁pomembno 52307 ▁מסי 52307 yici 52306 ▁ઓછ 52306 ▁pwede 52304 ▁xaqiij 52304 ▁പിന്തു 52304 quvvat 52303 ▁мазмұ 52303 ▁நிறுவன 52303 ▁արգել 52302 כיוון 52301 ▁Lää 52301 ▁gitti 52301 ▁براد 52301 ▁Zusammenarbeit 52299 ▁Ιστο 52299 ▁Пло 52299 ▁Stjórn 52298 əssis 52297 ▁ସଂସ୍କୃତି 52297 ▁güclü 52296 ▁dunn 52295 ▁ویبپاڼو 52295 らっしゃ 52295 ▁zadnji 52293 ▁imkon 52292 ▁лицо 52292 Sta 52290 غاية 52290 ▁રમ 52289 ▁Ụmụ 52289 ▁Emhlabeni 52288 ▁Овај 52288 ▁ନିର୍ମାଣ 52288 ▁économique 52286 ▁पाने 52283 ▁eksplo 52282 აშვი 52278 ▁جنھن 52278 ▁سماج 52278 adle 52276 ▁жні 52276 amisest 52275 ▁ಸರಿಯ 52275 ustur 52274 ம்பெ 52274 ▁estudiar 52273 ▁لوازم 52271 ▁Clas 52270 ▁dankie 52270 ▁depo 52270 வதும் 52269 ▁bieži 52269 ▁serii 52269 dech 52268 ▁Fá 52268 ▁tross 52266 ▁пака 52266 ▁doświadcz 52265 ▁szczegół 52265 ▁पौ 52265 ▁በተለያዩ 52265 ടിച്ച് 52264 ▁መም 52264 ▁Alber 52263 ▁അഞ്ച 52263 ▁закан 52262 стот 52261 ▁пътя 52261 ņām 52259 ▁ได้รับ 52259 гэж 52258 ▁روزی 52258 operi 52256 joties 52254 ▁quedó 52254 ▁sæt 52254 ▁команды 52253 ▁применения 52253 ക്കാന 52251 ຫ້ອງ 52251 ▁म्हणाले 52251 πλέον 52250 ▁טש 52250 ybi 52249 assist 52248 ilem 52248 মার 52248 ামূল 52248 ▁atkar 52246 ▁Аляксан 52246 ▁AST 52245 ▁žádné 52245 ▁lajeng 52244 ▁mastur 52243 ▁нэгдсэн 52243 ଳର 52241 ▁Select 52241 ▁ټکنالو 52239 ၿင 52238 ▁вроде 52238 ▁શરી 52238 ▁želj 52237 ▁maximal 52236 ▁politiske 52236 ediaan 52234 enire 52234 ▁Яну 52234 掃 52234 거운 52234 ▁divi 52233 ▁obliki 52232 ▁paradig 52232 ▁تنفيذ 52231 ▁دنیای 52231 ▁รา 52230 āzes 52229 ▁tunnel 52229 ላጎ 52228 ▁stanu 52228 ▁rigor 52226 ▁수정 52226 ▁begr 52225 ▁perfecte 52225 ▁칼 52225 ▁upoz 52224 ▁тонна 52224 ▁Steuer 52221 ▁મળશે 52221 энер 52220 ήλ 52218 haja 52216 ▁փաստաթ 52216 ▁moni 52215 रोध 52214 ▁Ministerul 52214 畢 52213 ▁BS 52212 eshay 52211 ▁هاش 52211 ώματα 52210 reden 52209 ହିଁ 52209 ▁cies 52209 壮 52209 thers 52208 ▁מות 52208 صدار 52205 čara 52204 ▁Homepage 52204 ▁캠 52204 ▁आफ्न 52203 ▁", 52201 ▁Kurz 52197 lipp 52196 ่าว 52196 ▁жүрген 52196 asm 52195 ▁јези 52195 ▁בניית 52194 ▁ფარგ 52194 ологі 52193 ดําเนินการ 52193 कृति 52192 ▁вези 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徵 35836 砂 35834 doms 35830 ▁ciudadan 35830 ल्म 35823 ნიშნ 35809 emplo 35798 itivos 35790 ▁meeting 35789 ▁մինչ 35788 ▁round 35775 껴 35771 ollow 35766 ଷ୍ଟି 35766 ▁توضی 35761 зени 35760 駄 35756 伺 35748 väč 35744 nız 35739 न्दै 35735 noló 35734 ▁trouv 35729 johta 35713 байы 35713 ர்த 35713 ▁भग 35708 irket 35707 անկար 35706 ວຽ 35703 咬 35702 ▁kids 35698 you 35694 มอ 35690 둥 35677 ଚନ 35676 laon 35671 ynnig 35671 捉 35668 ▁looking 35663 ünki 35629 wakho 35627 淨 35625 ធានី 35624 odnev 35621 ▁поддр 35615 elska 35613 疯 35610 ▁ойл 35603 ▁विस्त 35601 rị 35599 奮 35599 між 35597 ▁both 35595 ▁ለው 35595 ♫ 35595 шылығы 35587 飽 35568 ayanan 35567 躺 35535 ▁انقل 35531 ໂລ 35528 膠 35521 肺 35518 плу 35515 ិត្យ 35512 edlaethol 35505 endres 35497 დენი 35497 ດງ 35495 lumat 35492 engine 35485 ▁სია 35482 ligare 35479 癒 35470 nig 35464 pria 35460 ▁ประก 35458 okonzi 35451 িডিও 35450 ▁ಕುರಿ 35432 କାଶ 35429 ၳ 35417 屆 35416 잃 35415 හර 35414 ቭ 35406 葬 35394 стій 35388 杆 35388 郁 35382 ▁δου 35381 ịp 35369 ណ្ 35367 ಕ್ರಮ 35358 ្តេច 35353 ḷ 35351 ▁गल 35349 ipė 35345 omha 35339 ▁пси 35333 ឋ 35326 ▁kno 35315 ഈ 35314 ዳደር 35314 ▁affili 35307 νοδο 35293 ▁wygl 35293 cret 35287 덜 35284 tható 35281 ▁surprise 35272 ഡിയോ 35267 歸 35265 akume 35260 ▁dhè 35259 肢 35257 utions 35241 níct 35235 qinisekisa 35235 ▁rhy 35230 ၲ 35227 顏 35227 ediat 35225 ὶ 35217 ▁тәби 35216 ɩ 35212 ßerdem 35198 ▁Juvent 35191 ầng 35174 ▁compli 35173 ηρε 35169 ▁استف 35169 ດາ 35167 ɪ 35165 වර්ධ 35163 ապարակ 35155 ୃଷ୍ଟି 35140 ▁easy 35136 vire 35134 ▁wpł 35128 楚 35125 ▁تخصص 35124 ፳ 35121 ▁сере 35115 кімі 35113 ▁gevo 35107 ▁ପହ 35106 gyvend 35103 琳 35070 ▁ningu 35069 ▁ಲೋ 35068 勵 35057 ▁spend 35053 辽 35045 ՈՒՆ 35037 োস 35037 ▁gwy 35023 淋 35023 coin 35021 ▁umsh 35009 безпе 35001 лън 35000 厨 34998 ссиона 34994 orsdag 34988 뤄 34986 ▁tök 34968 ▁buying 34965 ▁command 34953 েব 34943 gris 34919 aboga 34909 നിക്ക് 34900 ▁Nutz 34879 柯 34852 eschten 34851 ▁نٿ 34848 ânia 34846 ▁election 34818 განი 34816 saí 34809 ▁Chic 34803 ▁improv 34803 ংলা 34799 ▁ଜିଲ୍ 34797 גרת 34779 ▁treatment 34769 ▁மட்டு 34768 ▁miemb 34764 ▁recoñec 34755 hors 34750 ▁nejs 34749 ზავ 34748 ▁ନିକ 34744 χρι 34743 ▁ေပ 34734 貓 34731 ▁installation 34730 抹 34728 kano 34726 ▁value 34710 پاڼ 34703 ੪ 34699 己 34694 ゃ 34691 ិវ 34685 noduch 34681 ਵਨ 34681 孫 34681 angsaan 34680 ногда 34678 ▁पिछ 34677 ▁Monday 34675 დეო 34673 ଥାନ 34662 reiwen 34660 ▁предприя 34654 柳 34654 รษ 34643 hamdulillah 34634 voju 34634 arrik 34628 歉 34621 註 34620 趕 34617 େଶ୍ୱର 34615 ھان 34613 ▁გარე 34612 eddi 34603 縄 34603 ুয়ার 34591 扭 34588 잊 34585 垂 34584 偉 34577 ▁wyją 34567 empres 34565 幽 34565 itea 34564 ▁appe 34559 ଫଳ 34557 عث 34555 ▁destination 34552 म्रो 34544 ▁ëffent 34529 แน่น 34525 ქონდა 34518 ▁Betr 34511 uluy 34509 훌 34505 ▁Тийм 34500 elitian 34499 ▁лау 34496 ▁chances 34486 adaş 34483 ▁thor 34480 줘 34477 涼 34471 elezo 34464 ▁پیشنه 34460 ត្ថបទ 34450 廃 34448 谐 34448 слави 34434 ພາສາ 34431 gomo 34430 ▁بڑ 34424 ▁গ্রে 34424 allist 34418 ρυθ 34418 畅 34415 ▁jobs 34413 panjang 34409 ▁πληροφο 34408 ագրել 34404 tsa 34385 ▁قوان 34381 ▁тодор 34378 ାରଣ 34372 ୋଲି 34363 嬉 34357 ▁शी 34353 ച് 34351 ▁difíc 34349 year 34343 ▁immedi 34336 ▁ब्रे 34335 ▁spenn 34318 wickl 34312 ered 34309 ▁Kdy 34307 olka 34304 skilt 34300 lüğü 34299 텐츠 34293 ‧ 34291 ▁окон 34278 섯 34255 偵 34246 ▁ಪ್ರಧಾನ 34220 ▁thuy 34218 پاڼو 34216 ▁Τουρκ 34216 ዔ 34200 ሊዮን 34195 ▁Mitarbe 34190 เภอ 34185 奪 34185 θαν 34184 อกจาก 34180 滨 34175 儲 34170 ▁ویژ 34166 ║ 34153 須 34150 okeo 34148 গ্রাম 34144 सला 34141 ುದೇ 34125 ρεις 34124 yday 34114 暇 34114 ▁loss 34105 酵 34105 끊 34104 irty 34100 jį 34097 婴 34091 nyango 34088 Эрд 34076 瘦 34069 ▁should 34058 1( 34054 ignet 34052 υτό 34051 ▁эсен 34050 ▁return 34045 ॉग 34043 tionary 34039 ▁تقری 34038 ▁нут 34031 ▁izvēlē 34020 stagram 34018 שיו 34017 ▁jump 34006 ▁préc 33999 혐 33997 ụs 33996 ▁reviews 33996 ▁istehs 33991 กว 33990 fọdụ 33969 ▁straight 33955 aterdag 33953 kušen 33937 縁 33926 驾 33926 ▁lapt 33900 ैनिक 33893 詰 33888 ▁sadi 33886 凤 33883 亏 33876 ▁definition 33875 adawan 33860 өкүмәте 33860 ▁vəz 33856 penn 33850 ierre 33844 ▁ऑन 33843 锦 33839 フト 33830 ▁atan 33801 杉 33794 нечно 33793 wih 33790 ▁репре 33787 ▁Αθή 33784 ર્તા 33779 ustru 33776 曝 33774 ▁ones 33770 Wiki 33766 sjed 33754 ೇಜ 33741 ▁γεγον 33740 ▁island 33728 ▁ଅଭ 33725 ใหญ 33722 ▁flow 33722 stāv 33715 ▁παι 33707 ریع 33705 Ử 33697 ീപ 33694 ▁පාර් 33686 cook 33683 雕 33681 APO 33677 ▁Sağlı 33677 ▁unters 33675 须 33675 ▁සිං 33673 ▁again 33671 낭 33658 следования 33648 ▁ग्रा 33646 ▁októ 33632 ፖሊ 33629 ữu 33620 λέγ 33617 겸 33613 祉 33612 ួង 33610 ▁cook 33590 ‒ 33589 ኀ 33585 33583 妮 33561 ▁ევ 33559 ▁mean 33552 ଈ 33543 قای 33534 ணைய 33533 จัย 33524 慌 33512 ▁magic 33508 ▁desarr 33505 ▁zák 33496 útbol 33495 éas 33484 ▁тей 33482 ័ព 33477 懸 33474 ตรียม 33471 тінде 33466 ▁saving 33465 翰 33449 oshop 33446 ੰਡੀ 33442 èixer 33440 싼 33440 használ 33428 ▁szen 33413 ନ୍ଦିର 33412 ይማኖ 33404 тня 33402 ▁Gesund 33400 វិទ្យា 33388 ▁فرهن 33388 ponina 33383 ▁season 33382 Ữ 33378 ▁dental 33378 潤 33372 ▁კიდე 33368 ▁människ 33367 faat 33359 idianana 33359 叹 33350 セン 33347 啥 33347 wiście 33342 叙 33338 sterreich 33335 ▁μεγαλ 33322 ृह 33316 fox 33313 eschter 33312 zació 33310 ▁wię 33310 wodraeth 33298 አዴግ 33280 ータ 33278 jorn 33265 элц 33263 cked 33249 өлгән 33249 ፏ 33248 ازه 33247 liotek 33228 ünkü 33224 ayithi 33220 ▁Armen 33219 努 33215 룡 33211 ழா 33205 ឪ 33196 өнки 33190 阪 33189 бва 33188 harib 33184 ▁Všech 33183 ლობაში 33177 ▁consec 33176 สือ 33171 ▁accessible 33170 ərk 33169 ▁welcome 33169 欄 33168 ऑ 33157 ▁ערי 33156 ▁anál 33154 ▁versions 33152 ◎ 33149 ▁уҡыты 33147 kungan 33144 ▁السا 33143 ▁реє 33140 wic 33139 idhlig 33131 ▁rije 33122 ▁хви 33120 逢 33114 ▁الاجتما 33102 颁 33102 蒸 33099 ▁kick 33095 ▁nü 33095 쯤 33090 unty 33085 ☞ 33083 ▁behal 33081 덴 33080 ▁Daarna 33079 isig 33074 ※ 33073 ▁Only 33073 uhlang 33067 泛 33064 កួត 33061 ▁תג 33058 坂 33054 ▁دقی 33037 ഢ 33033 bigan 33024 ▁leu 33016 gress 33013 ▁පැහැ 33005 langk 32993 ▁chwara 32988 ▁Љ 32983  32982 ariki 32963 ▁ذریع 32958 碗 32956 koani 32953 ászól 32953 ▁looks 32952 ▁태그 32947 ေခ 32942 욱 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30744 ﴾ 30738 What 30733 oileadh 30731 ▁tiế 30728 ▁Right 30716 Xhosa 30706 ▁ଓଡି 30706 ▁random 30702 ▁йүнә 30701 ▁award 30694 ównie 30681 ▁When 30680 doğan 30679 oguć 30668 ▁patients 30661 ่วน 30660 마트 30655 ካም 30626 ▁hüqu 30626 ▁нэв 30623 понен 30621 -295 30620 ▁പഴ 30619 吳 30619 jamin 30616 痫 30614 줍 30601 虛 30592 emmeng 30588 翔 30588 랭 30588 ▁svilup 30583 斑 30582 寬 30576 ęki 30569 龄 30563 лб 30561 ▁sver 30560 ▁yerləş 30557 粮 30556 idheachd 30553 ▁along 30547 ▁nuev 30547 ▁podsta 30534 ▁нута 30533 ▁مدی 30533 более 30532 ▁sveikat 30529 ▁setting 30528 ▁development 30512 糟 30512 ▁taken 30507 የሱስ 30489 ന്തു 30483 ▁жүргі 30465 朴 30462 뭔 30453 ▁münasib 30446 ▁Vò 30444 bbbb 30416 逸 30413 剪 30411 ክራ 30409 ंग्रेस 30402 ออน 30402 ▁நிறுவ 30401 멤 30390 ▁уа 30386 ▁다운 30384 पना 30382 痕 30363 ▁kakov 30354 ▁محتر 30354 ▁qen 30353 któ 30338 ᅳ 30337 ▁ಕಡಿ 30327 αγγ 30316 찍 30311 yscy 30305 োষ 30305 닌 30302 λλην 30301 funn 30294 ▁Upor 30286 öző 30268 ▁Committee 30266 ▁without 30244 ίντεο 30234 ▁summer 30227 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29826 ▁study 29808 ្រូវ 29807 ▁football 29803 råde 29795 deithasol 29791 ▁button 29776 ὸ 29761 ▁miejs 29750 ገሪ 29749 ▁tiny 29742 ▁ಪ್ರೊ 29733 ▁fles 29730 әсәй 29726 oted 29725 悦 29725 坡 29721 緣 29719 τωση 29718 kvæmt 29714 ▁секо 29711 Ɛ 29707 盲 29704 ▁աջ 29701 ▁around 29698 kvæm 29691 瘤 29688 ▁spænd 29686 ктів 29683 ▁هغ 29682 ▁рекомен 29671 ѕ 29667 ▁yuq 29666 शेष 29664 artic 29659 ռն 29658 මාත් 29657 นด์ 29657 ▁Devel 29643 ▁اقد 29643 ▁मुस् 29641 FORM 29639 憾 29638 ững 29635 赋 29622 ื่อง 29616 몸 29614 挨 29608 ▁ಗೊ 29606 ▁սկ 29592 ▁tull 29584 ំផ 29579 ▁commission 29573 χνο 29570 ▁Whats 29570 ્વા 29567 駆 29567 ledge 29555 妝 29547 衷 29547 ▁Qeer 29546 тего 29538 谨 29536 ▁lots 29524 ▁پژ 29524 壇 29523 ▁chuy 29512 ▁प्रच 29507 ▁reta 29489 ▁πίσ 29487 ร่าง 29486 ▁eat 29481 澤 29477 ▁rahvus 29476 ෙන්නෙ 29471 ørdag 29468 ▁Fry 29464 쌓 29460 ikbaar 29455 蜂 29453 ▁enjoy 29448 ▁términ 29436 ▁Beis 29435 ▁müh 29430 蛇 29428 isiaj 29418 ▁സ്വന്ത 29415 ▁sklop 29410 ▁verschieden 29408 谱 29402 毅 29399 paña 29383 rojt 29380 ▁issue 29372 ස්තර 29370 arshi 29366 ▁sharing 29365 ៌ 29360 ضان 29358 ▁sicr 29358 ▁اطلا 29350 ▁breath 29347 ▁sge 29341 zä 29340 çok 29340 შუ 29326 ▁آغ 29315 unning 29311 옮 29308 tschaft 29301 ▁draw 29299 որձ 29287 ဖွ 29277 餅 29276 ▁декла 29274 omiast 29270 ▁third 29269 ▁Pemer 29268 ῦ 29266 ▁Сь 29261 өйөм 29242 ▁сығанаҡ 29240 nner 29237 ▁βο 29221 წყო 29219 យោបាយ 29215 脉 29213 逮 29211 ፖለቲካ 29210 ዣ 29206 ▁pomi 29203 taare 29202 끗 29191 ユー 29188 isisekelo 29187 溶 29181 ▁++ 29179 鬥 29173 ▁تفصیل 29170 륭 29163 ॰ 29162 狭 29157 멜 29147 襲 29146 戈 29138 ▁कवि 29132 ▁provenç 29129 ανεπισ 29121 ▁photograph 29119 ▁things 29111 暫 29110 炮 29109 誇 29104 βάλλον 29098 ▁stør 29093 ▁technology 29092 ▁συμμετο 29083 샘 29083 amnat 29081 ▁şö 29070 әрби 29069 ▁expand 29067 옛 29040 evens 29037 ▁academ 29034 ਕਤੀ 29032 jalanan 29031 ‘ 29029 😎 29022 yisa 29016 مدة 29016 ▁jellem 29013 浏 29007 ▁भन् 29003 ▁Things 28994 лкова 28993 ▁loy 28980 ាយុ 28975 ▁qəd 28974 霊 28970 😘 28970 ▁सार् 28969 othing 28966 덧 28963 柴 28960 taar 28958 😆 28956 멸 28950 යුතු 28942 ესო 28942 ▁thab 28935 werf 28916 ▁Lach 28913 ▁PROG 28913 μιουργ 28911 ▁Keep 28902 iekš 28897 ▁다양 28896 ▁gets 28888 Chat 28877 ▁switch 28876 ▁wyglą 28876 润 28875 ບຸກ 28869 팁 28865 桂 28860 сред 28859 ▁прадста 28858 밑 28856 ଖ୍ୟ 28854 өлб 28852 ▁Yet 28844 jder 28842 秉 28838 otball 28837 腔 28823 តី 28822 Монго 28819 疏 28810 údo 28808 ▁lifestyle 28806 ▁dhin 28800 뒷 28799 ▁اسڪ 28797 nels 28787 ▁ਤੁ 28783 凉 28781 튀 28771 yní 28767 ınıf 28755 吊 28754 ▁জু 28746 덤 28739 ฑ 28738 踢 28738 ▁wirk 28737 ▁woman 28733 ▁اڃ 28728 ▁қалып 28722 zysk 28719 yslân 28699 Ĺ 28682 逻 28679 блиоте 28677 ापन 28676 upunki 28665 擬 28664 玲 28655 χαρισ 28645 thing 28644 ▁መካ 28643 尘 28641 ▁walk 28633 ▁izgl 28629 ▁hjel 28627 йынса 28612 ▁masar 28606 κεκρι 28603 듭 28601 omini 28582 ▁moguć 28574 ▁circum 28560 グル 28559 च्च 28552 णाली 28547 辩 28546 曹 28544 萌 28538 顆 28538 薩 28533 丘 28531 엽 28526 ▁thread 28522 eraus 28520 虽 28515 fornia 28507 tör 28505 скольку 28500 ▁յուր 28499 ▁ସୂ 28495 ▁øje 28494 धील 28486 ▁Flü 28480 ridge 28469 ▁ಎನ್ನು 28469 ຫມົດ 28461 芯 28461 钻 28455 ▁لاک 28453 ▁clean 28448 ഷേ 28445 ▁dettag 28442 ▁risult 28441 ▁beson 28433 挪 28426 жере 28425 ぷ 28413 勿 28403  28398 iecie 28391 忌 28388 agaimana 28386 scri 28386 ▁өд 28386 otud 28375 ্ঠ 28370 况 28370 仇 28361 ၥ 28360 ີໂ 28352 hadh 28348 ▁birthday 28348 ଶ୍ୟ 28340 ▁बिक 28335 িন্ন 28331 ▁termész 28328 डेल 28324 ដឹក 28316 ំពេញ 28312 罰 28301 阅 28300 拨 28293 คคล 28291 ᅮ 28276 Ά 28272 ଦ୍ୟା 28266 ildə 28256 ública 28255 擺 28253 ▁Why 28248 Ș 28247 лові 28242 ▁тәь 28239 ▁جگ 28236 憲 28236 Ψ 28228 izmat 28225 ፐ 28221 ੫ 28216 ▁wood 28213 abled 28197 úka 28197 urate 28189 ▁wont 28187 妆 28187 loq 28182 senal 28182 ▁generation 28182 賓 28175 톱 28174 로그램 28168 槽 28164 ুদ্ধে 28153 √ 28153 дется 28142 զում 28134 ▁Ders 28132 ▁something 28131 spaper 28128 팝 28126 śnia 28124 avon 28118 हेर 28111 ▁beat 28108 ຄັນ 28102 ▁Сабақ 28099 ▁indifer 28092 쉽 28084 ▁هوند 28079 стоян 28073 ▁slow 28070 েষ 28062 釣 28059 ▁seconds 28038 ▁sessions 28028 꿀 28024 麥 28023 屁 28016 ampuan 28011 ugenzi 28010 앞 27989 ivavy 27988 ▁sė 27985 ▁ممن 27979 〈 27975 ▁ամբ 27974 ▁maalis 27956 簿 27956 ydym 27955 ▁Испа 27955 ▁beautiful 27939 тәш 27923 awn 27906 ▁nadzie 27906 吻 27888 ത്രീ 27885 ▁innovation 27884 ekaar 27878 ▁MAF 27865 疗 27864 idupan 27852 ▁නොම 27839 딜 27838 乏 27835 ▁امتح 27834 ▁sécur 27824 ▁ସୁର 27823 ▁؟؟ 27822 荡 27822 赁 27821 ਤਾਨ 27818 ▁building 27807 ▁demasi 27804 etahui 27801 綜 27799 搶 27786 ▁grud 27785 ملكة 27782 ▁Украи 27779 ▁پہن 27779 믹 27777 팬 27777 ▁अझ 27770 ywedodd 27769 ▁ਪੁਲ 27769 ίτε 27767 깨 27767 ▁Yüks 27762 董 27761 ፆ 27759 τίου 27750 ▁sema 27746 캡 27741 inique 27740 cedes 27737 ▁suppli 27736 ិច្ច 27727 ▁زندگ 27720 ▁path 27710 ▁близь 27705 ▁Согла 27698 àc 27695 ▁phys 27677 jid 27676 ▁eG 27675 ▁asil 27651 ▁svij 27648 ▁ages 27647 慧 27647  27643 ᄒ 27634 Ham 27629 ▁Then 27623 纵 27611 αγγελ 27610 òde 27607 勾 27605 斜 27601 ▁between 27598 锻 27596 usätz 27590 buzo 27576 ɑ 27555 ঢ 27552 긍 27543 ylland 27542 эхдээ 27539 嗯 27521 惹 27520 ລັດຖະມົນຕີ 27517 蓮 27516 elson 27514 逾 27514 ▁js 27504 ▁χαρακτη 27500 凍 27493 nbspnbsp 27492 邓 27488 겐 27483 贺 27482 พันธ 27475 شدد 27469 eful 27468 ೃದ್ಧ 27468 ▁challenge 27461 ▁kalab 27453 ▁ዙ 27450 閒 27449 ómetros 27448 ဉ 27446 ្ធ 27441 涌 27441 ▁ವಿಶ 27438 ▁кәсіп 27427 كتور 27426 ਗੜ੍ਹ 27424 ▁նախատես 27418 孝 27416 ▁šte 27405 ▁Өф 27405 ቿ 27400 abog 27398 ▁करी 27387 ▁импе 27374 狱 27372 wnia 27369 ▁පළා 27366 낚 27362 ▁کیل 27361 숲 27352 wéck 27344 stract 27336 ▁nauc 27336 amnă 27332 ▁sleep 27327 滚 27325 ftware 27312 kles 27304 auty 27298 ▁Հանր 27292 ્ન 27290 켰 27269 անս 27266 əmiyyət 27265 濕 27265 ǎ 27244 ឿង 27239 ഹൃ 27233 惧 27230 梨 27230 ▁κορ 27228 ՙ 27226 営 27205 кљу 27204 ahagian 27181 ต้ 27178 REE 27145 剣 27145 ▁ალბ 27144 dụ 27141 ▁ваҡ 27137 ▁perspec 27122 齊 27122 réck 27120 ahari 27118 ਝਾ 27118 alék 27116 лексан 27113 ▁Duni 27106 ▁range 27101 貯 27091 ▁technologies 27089 ▁মাধ্য 27080 enangan 27078 ▁зорил 27074 نامج 27068 ▁மாவ 27062 ผ่านมา 27060 ▁takmičen 27058 膨 27055 ▁զբ 27046 ▁შესაბ 27045 ▁ನನ 27039 ▁hlu 27036 alisar 27035 minat 27032 neze 27030 ▁бюд 27026 yük 27023 venes 27016 ŭ 27016 ▁fráb 27004 ▁luck 27004 暨 27003 ြား 27002 junt 27001 韦 27001 ော့ 26988 豫 26988 nyddio 26987 tuat 26984 ሡ 26980 ▁Köl 26980 棚 26978 ධිපති 26976 ილე 26974 يير 26949 afima 26947 ▁Lling 26944 ▁저작 26924 ▁Дмитри 26922 ▁tətbi 26921 ፩ 26912 楊 26910 ▁milim 26908 Ặ 26904 尸 26893 ▁оҙ 26886 ăzi 26882 Ắ 26881 ▁байд 26875 стоў 26873 ▁исполь 26870 Ճ 26863 ▁cadr 26862 ▁planning 26861 ▁współpra 26861 มพ์ 26839 бук 26835 ▁аюул 26833 輔 26831 ▁tytt 26826 ✨ 26825 俩 26821 gesamt 26808 ลิป 26801 ዖ 26788 聪 26784 ्वि 26775 řízení 26774 ∙ 26773 ▁enterta 26773 ául 26768 genes 26767 “ 26766 ▁fresh 26761 롬 26760 ▁አገልግ 26757 坑 26749  26743 ▁trade 26741 ्यों 26733 彭 26730 ▁zhv 26728 kaar 26725 iyesi 26724 ▁तसे 26709 क्की 26708 ▁crow 26708 遂 26703 ▁நடை 26695 ▁សេចក 26695 ▁پرون 26687 ▁केंद 26679 hibe 26661 ▁ചെയ 26661 ุตบอล 26660 膀 26656 ▁судал 26654 akatulong 26650 শেষ 26650 uries 26646 ግዚ 26645 วิร์ 26644 ▁สร 26642 ؒ 26640 泌 26632 ▁updates 26624 汰 26622 ઍ 26613 ▁হিস 26613 ▁aspet 26610 贯 26610 incane 26609 ▁пісь 26609 วัติ 26605 ▁applications 26605 ▁government 26605 Ừ 26601 弥 26600 ▁having 26594 ▁հայտ 26593 凶 26586 ▁ລາຍການ 26585 ▁since 26582 獻 26580 jenzi 26576 ഛ 26576 棉 26574 閣 26565 ▁ေန႔ 26562 ▁መሠ 26562 ikaNkulunkulu 26558 ▁Tuesday 26555 ▁field 26552 ברסי 26550 ▁pej 26540 pload 26532 łącznie 26527 骂 26520 ୟକ 26519 pped 26518 歧 26511 闘 26498 盯 26496 ▁delivery 26476 ▁request 26476 ammans 26446 Ѕ 26426 ▁εξω 26412 ▁kesel 26406 ▁else 26393 ષા 26389 ▁циви 26384 ぴ 26383 臓 26380 샌 26369 ુંબ 26339 ▁რამდენი 26338 ▁عباد 26337 ▁แน 26336 руа 26329 ғалім 26329 esiog 26322 ▁morning 26322 ▁ہون 26317 잖 26303 unyai 26301 ▁interest 26295 ərç 26292 ogrā 26283 σιμοποι 26274 벼 26274 凄 26262 ▁municí 26257 мәте 26256 ฏิ 26253 ▁વિશ્ 26253 spev 26252 巷 26248 atste 26243 ▁kuto 26240 ▁කාන් 26236 辰 26236 ඞ 26230 ັກງານ 26229 ▁yik 26221 쌀 26220 ◇ 26218 ष्ट्रिय 26216 kohol 26212 ▁Columb 26209 ើក 26206 ▁khuy 26193 อุป 26188 ▁excell 26187 standing 26186 ▁going 26184 elligen 26182 inment 26182 ▁તૈય 26182 떡 26182 辖 26180 섰 26174 uuna 26167 ▁left 26162 zenta 26159 ੇਸ਼ 26156 ಮುಖ 26147 ▁empfe 26145 ▁pening 26145 सिल 26135 श्चि 26134 ஓ 26131 遮 26129 ▁मद 26125 sait 26116 ඊ 26111 織 26105 hụrụ 26102 ▁капа 26096 ịtị 26095 furt 26092 ▁marras 26086 ▁ეროვნ 26080 alani 26073 ▁ಉತ್ತ 26069 ট্ট 26065 enging 26046 ▁স্ত 26044 стрэ 26038 걷 26037 ▁wür 26023 ungsa 26021 ▁төлөв 26017 ଲୀ 26007 संद 26003 ▁basic 26003 ulie 25993 ▁ಪ್ರಶ್ 25992 葛 25986 تاح 25979 賴 25978 થમ 25977 丧 25975 ստեղ 25970 멍 25964 ftrag 25963 ▁funny 25958 աճառ 25955 elder 25938 ▁ปร 25938 даря 25935 ▁notice 25928 zeda 25926 乖 25921 哀 25907 յութ 25904 틸 25904 挡 25901 ▁opening 25893 աձայն 25891 꾼 25888 ▁why 25885 齡 25845 ructura 25844 〔 25837 〗 25836 reman 25832 Ƙ 25832 饱 25832 〖 25828 elain 25826 ▁պայ 25825 paq 25815 瑟 25814 喷 25810 ▁באמצ 25806 odaeth 25799 գային 25781 ันธ 25779 ▁asl 25762 徳 25761 찾 25757 싫 25754 ▁ପ୍ରେ 25750 จจุบัน 25748 炉 25747 艘 25743 庞 25740 烂 25740 ερι 25732 πόν 25731 〕 25731 ▁inse 25730 ርስቲ 25729 ▁advis 25724 Ů 25692 taient 25691 ▁جګ 25690 ভাগ 25684 ທະວີ 25682 恥 25678 ▁σπί 25673 כויות 25672 ▁mother 25669 👇 25668 กฎ 25663 jumlah 25660 ମାର 25659 nologiy 25641 帖 25628 дование 25618 诈 25616 廚 25605 騙 25605 掏 25601 اشت 25590 匯 25590 ՄՆ 25567 ၎ 25562 删 25557 掩 25551 क्षि 25550 uali 25544 ▁aix 25544 挣 25539 ▁неме 25535 ♀ 25534 ▁آزم 25533 紋 25531 왼 25528 ▁βρί 25527 ▁zbo 25526 անչ 25524 렀 25523 полне 25509 ▁effectiv 25499 逝 25491 ଭାଗ 25488 ▁อํา 25486 ▁پولي 25483 ಂತ್ರಿ 25478 νεργ 25477 ilson 25474 ▁Finfin 25474 艦 25474 狙 25473 ▁death 25467 ▁mainten 25465 咪 25465 昏 25464 裸 25460 issas 25457 ▁säh 25452 klär 25446 pòt 25446 ▁tickets 25444 ▁hous 25441 ாய்ப 25432 ▁ڈاک 25432 Ễ 25430 ▁Oblig 25430 angor 25412 ▁іно 25411 닭 25410 ἀ 25409 ▁memory 25404 ▁تلویزی 25402 undation 25400 ριακή 25398 ▁በሙ 25396 ▁sources 25395 වරණ 25390 ▁дефи 25371 ▁ඉන්ද 25371 ▁تاری 25368 賢 25365 誓 25364 舟 25362 ੍ਰਿਤ 25361 堵 25357 ▁կիր 25346 ▁town 25343 ▁paslaug 25341 іх 25322 ▁काठमाड 25319 閲 25319 ▁storit 25313 ▁رہن 25312 gelig 25309 ▁itiliz 25309 슴 25292 趁 25290 udukan 25285 ömr 25280 ▁initial 25279 τρον 25274 뽑 25273 angla 25270 беля 25268 ▁results 25263 ▁practice 25260 ちら 25250 ysyll 25248 եխն 25242 dracht 25229 ጴ 25225 ଣ୍ଣ 25213 ịt 25202 巾 25194 ▁Camer 25192 λεύθε 25191 ▁Поро 25190 ▁अगा 25190 itiiv 25185 稚 25185 ئیس 25184 kék 25181 यान 25173 ठन 25172 ▁expertise 25168 ▁równie 25168 хгал 25164 ▁তৈ 25159 锐 25155 靴 25147 ങ 25145 plice 25143 ▁אָנ 25141 ठमा 25139 ▁learning 25137 ▁აშ 25136 yeutu 25135 ▁ಕಾರ 25134 ▁Elek 25133 ▁presentation 25131 譯 25127 ▁മുഖ്യ 25126 臂 25122 abei 25119 etahuan 25112 ୍ନ 25111 ござ 25111 耕 25108 晒 25107 teix 25097 iving 25082 挫 25080 ➡ 25074 ▁kawa 25072 Ở 25052 ▁ფო 25048 ▁different 25044 ▁själ 25041 ▁Επί 25038 茂 25036 ▁ಲೇಖ 25034 潛 25033 퀴 25033 煌 25032 盼 25025 kvæ 25023 🙁 25023 طني 25018 ▁weeks 25016 陀 25014 织 25008 ▁myself 25005 캔 24996 clos 24970 ▁uses 24968 ▁სის 24968 爺 24960 ңел 24952 ▁ardı 24952 phere 24951 ჯახ 24950 ▁Than 24947 श्यक 24938 ▁sīk 24938 ▁Every 24936 ▁Hercegov 24924 плика 24920 hethwe 24916 ังหว 24912 ▁ezink 24910 iebie 24905 奢 24893 ọi 24885 ▁They 24885 ▁протя 24885 ▁мян 24883 שרא 24880 շխ 24879 ▁models 24870 낄 24869 ▁ഒഴി 24865 ដា 24857 ▁owes 24840 urity 24838 ▁mjese 24838 egól 24832 μερί 24829 ▁paint 24829 هنجي 24827 ▁βρίσκ 24822 რც 24820 funden 24819 ▁ημέ 24814 ▁иғ 24814 ▁proud 24811 ریکی 24799 hány 24795 ▁ଭିତ 24789 岳 24785 ξύ 24781 ▁রহ 24772 ▁hour 24767 챔 24756 ▁counter 24750 ଼ 24749 蹈 24744 adené 24743 搖 24730 ượ 24727 istoire 24725 ▁ਸਫ਼ 24720 DNES 24712 ✿ 24712 ▁प्य 24710 саг 24702 ikiai 24695 虐 24689 etah 24688 ▁შეიძ 24684 ترن 24683 滩 24682 zības 24676 лкото 24672 udoom 24669 馈 24654 ▁safe 24645 ▁марке 24640 ▁гүй 24630 ▁исе 24630 賠 24630 ▁sets 24619 ▁Бөг 24617 ekkür 24610 ▁එරෙහි 24593 лимпи 24581 ▁happ 24580 ƒ 24569 ▁সাং 24569 صیل 24564 więks 24561 λαδή 24561 ▁mütləq 24550 눌 24542 ▁zitu 24540 렛 24540 ▁ngokwez 24538 ▁प्रस्तु 24536 肿 24527 ▁Some 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20296 ▁another 20294 EFA 20292 ętr 20282 アクセ 20282 ▁бизни 20280 胀 20279 壤 20273 arlama 20263 thur 20263 ▁Chall 20260 큰 20254 ▁өнд 20253 蘋 20253 ဧ 20246 ▁aranje 20241 ხვა 20239 çə 20232 ▁پښ 20211 僚 20207 vijek 20206 laşık 20196 ▁voice 20195 αιδευ 20194 ▁Qe 20192 弯 20179 ▁преи 20172 絞 20161 ▪ 20152 ▁bibliot 20151 ▁repe 20149 澡 20140 िकेट 20137 erluan 20136 صورت 20132 筒 20131 ▁മനു 20129 畏 20113 ▁шө 20112 ؑ 20109 娛 20106 ▁wait 20105 ▁Janu 20104 ▁객 20099 ष्ट्रीय 20093 لاین 20086 碌 20085 анбаатар 20083 талия 20082 ▁Wux 20082 冻 20079 퓨 20075 侨 20064 umpur 20062 锡 20058 anethi 20057 ▁ભાર 20044 毕 20038 ▁ejerci 20037 ĺžka 20035 뻐 20032 ərbay 20030 ▁içeris 20029 ὰ 20024 ▁ընտ 20022 ufig 20019 亭 20018 пълно 20016 ਿਸਤਾਨ 20016 撰 20016 ▁пожалуй 20014 ぇ 20014 кінші 20006 ž 20000 霜 19997 imbali 19994 ▁Mitgli 19993 ▁करि 19987 ▁obzir 19979 猎 19973 ườn 19970 핏 19970 甸 19966 ▁ঘোষ 19960 鹏 19958 ujarati 19957 闷 19951 λήματα 19945 ither 19935 essous 19921 erant 19920 ganu 19920 ଧାରଣ 19919 aving 19918 เกษ 19917 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僵 18663 ▁running 18660 adingeka 18658 晩 18655 ▁ڪور 18654 gjeng 18652 ▁breakfast 18650 ▁үҙә 18648 owiem 18647 ▁біблі 18645 ▁entèn 18641 踩 18639 ▁friendly 18637 驰 18630 თამ 18627 ▁These 18624 աքր 18611 ▁separate 18609 傢 18604 瑰 18596 jourd 18591 歓 18590 ▁нрави 18572 渔 18572 ▁ezinj 18569 volv 18566 ▁മറു 18561 젖 18550 දිහ 18546 ▁ନ୍ୟୁ 18544 กล้ 18538 联网 18535 ▁നന്ന 18533 夥 18528 ugcin 18522 cesz 18507 akale 18506 ▁ቋ 18500 駛 18497 慨 18492 यं 18491 لكتر 18488 ولايات 18481 ằ 18479 ▁ແອ 18470 坛 18470 ▫ 18463 ヶ 18453 velopp 18449 बीसी 18446 πουργ 18437 挽 18426 ौन 18416 ▁everything 18413 ▁แห 18413 曉 18411 oile 18398 วัล 18398 ▁איינגע 18398 덮 18397 େଷ 18387 ▁салба 18383 霧 18383 ੈਨੇ 18375 cimbi 18371 ▁told 18371 ▁Srbi 18366 ▁scegli 18366 ıll 18365 ▁dipak 18362 ▁iT 18360 躁 18351 崛 18345 ▁ସଂସ୍କ 18342 〇 18339 歇 18339 ▁хөт 18338 蟹 18331 ementerian 18323 ▁ακό 18322 ☛ 18322 譜 18320 憩 18318 sainväl 18307 fungsi 18297 맑 18294 ▁mbal 18292 ▁Mawr 18291 ਅਦ 18290 枯 18285 ▁විර 18277 섹 18276 騒 18274 拶 18273 숨 18266 erous 18263 cicleta 18262 ῶ 18260 軒 18252 ່າວ 18244 змо 18237 ありがとう 18225 ▁knji 18222 ▁उम्मेद 18220 埼 18207 ▁ანგ 18204 නිල් 18201 ากาศ 18201 葡 18201 ဍ 18195 ਿੰਡ 18186 ▁Waaqay 18186 삽 18181 앗 18179 砸 18178 爪 18176 actur 18170 ▁ət 18167 یث 18152 ▁given 18148 ቕ 18143 눠 18142 ▁Johns 18140 雀 18140 ▁gone 18134 ▁କଂଗ୍ରେ 18132 ovaž 18129 脾 18128 oltà 18126 ▁වෛ 18125 ▁иғти 18122 aneiro 18116 遷 18114 쏘 18104 onnert 18098 ▁неско 18097 ઔ 18093 αιδ 18090 баҫ 18090 خواست 18088 ▁issues 18086 ♂ 18084 ▁duu 18081 謀 18078 തല് 18074 ▁öyr 18074 मीर 18071 ٴ 18070 ણાવ 18066 ઐ 18065 ابرا 18064 쁨 18062 斌 18059 ਭੂ 18057 尴 18057 ános 18047 ικονομ 18040 ▁למצ 18039 ▁አገል 18034 ▁حاک 18033 玫 18027 ▁frança 18025 枕 18018 ▁Адна 18013 ▁sgeul 18010 ▁crypto 18004 ▁ସମସ 18001 鳳 17990 niagaan 17986 ▁குழந்த 17985 ▁taari 17984 javí 17978 ង់គ្លេស 17976 attoo 17967 ▁collect 17965 ▁orq 17964 夾 17964 ျပာ 17959 ▁দক্ষ 17957 ▁reve 17952 ▁leave 17950 懒 17946 orrow 17945 ੭ 17944 gə 17941 ள்வி 17941 adaxwe 17937 ▁polj 17935 ∈ 17932 ▁manufactur 17920 吾 17911 arici 17903 ниција 17903 阐 17898 ▁gema 17883 ▁during 17872 벅 17872 ້ອ 17871 œur 17868 ؔ 17868 袁 17864 ՚ 17857 ଦ୍ଦେଶ 17842 bọchị 17836 螺 17836 Ẻ 17835 është 17833 runaan 17827 ▁انٹر 17827 塾 17817 ezici 17813 ulous 17812 ▁exercise 17809 ▁Meen 17805 윗 17805 亩 17804 곽 17803 湘 17800 ▁related 17799 Ќ 17792 梳 17789 ▁thro 17770 ▁гэм 17764 ▁მოძ 17763 妄 17761 ▁məsul 17760 ੯ 17752 ▁ڳو 17750 ▁writing 17745 劝 17739 where 17732 ▁Pors 17731 σωτερ 17728 विद्यालय 17721 ▁clear 17719 ሥት 17715 凸 17705 πτωση 17704 ▁determ 17702 ▁ຕິດຕາມ 17691 raeg 17689 ੮ 17681 ▁kitchen 17680 ongozi 17678 圏 17668 amez 17666 říklad 17665 Ň 17640 ▁factors 17638 镑 17633 шру 17631 álisis 17630 әүеф 17620 ▁Febru 17609 اهش 17605 霞 17600 ▁thon 17597 ▁tetik 17596 ゥ 17591 ▁पृ 17588 ▁człowie 17584 ▁যৌ 17579 สัต 17564 ▁supply 17562 ▁გიორ 17562 벳 17555 amtaare 17541 ინც 17535 чч 17531 ▁updat 17526 ▁eskor 17523 ▁wux 17522 兑 17522 涙 17522 движи 17521 हास 17520 ิตย์ 17520 ▁forward 17517 ▁éx 17517 ември 17511 ැනි 17505 곰 17505 ▁බ්ලො 17497 disho 17493 珊 17491 توبر 17482 缝 17475 vious 17470 捏 17467 ▁کامی 17465 ▁ବିର 17464 剥 17462 raživ 17461 姚 17460 ເຟ 17457 ▁effort 17455 veck 17454 ábbi 17448 닛 17446 ▁egent 17437 ábban 17432 γκό 17432 郷 17432 츄 17419 ตัดสิน 17415 醇 17403 ቐ 17401 ydessä 17398 ▁struc 17394 ▁means 17392 ▁wine 17392 ▁lapor 17389 쉐 17388 Ỗ 17386 attro 17378 飘 17378 퀘 17373 르면 17372 宛 17366 ટેલ 17365 婷 17360 haupt 17352 ଂଗ 17345 唤 17340 띄 17333 råder 17330 utschen 17323 弦 17311 繞 17296 byll 17294 톨 17291 ▁takes 17288 窮 17283 ▁including 17274 瞧 17274 ▁mañ 17268 न्दी 17264 ▁சமூ 17263 墅 17255 썼 17242 ₩ 17237 følgelig 17236 蟲 17235 ▁Tysk 17233 ্তু 17229 瀏 17229 橫 17228 ▁भ्रम 17223 iegész 17211 耍 17191 ilable 17179 菊 17175 瘋 17167 骄 17165 沫 17163 껏 17158 ungtor 17153 ▁sëm 17150 ≤ 17145 ्यालय 17135 ▁गोप 17132 ๋ว 17119 եւէ 17115 стрычні 17105 ▁ವೇಳ 17103 鄰 17097 ონომ 17096 ▁assistance 17092 Thi 17081 ▁nouve 17079 ▁Необ 17071 ൦ 17070 ঈ 17067 μεριν 17066 ່ຽວກັບ 17054 ▁rego 17053 ▁முய 17053 饿 17053 ▁bura 17050 崔 17050 ▁worth 17048 ဿ 17046 淀 17044 uerwen 17043 揉 17042 ਬਦ 17034 抄 17029 ▁comes 17026 翠 17025 ▁ww 17022 ▁жоспа 17022 ▁जाहि 17022 ειτουργ 17015 ▁ship 17014 ດ້ 17010 ▁skills 17006 淚 17006 wyan 16993 ▁fantastic 16992 ▁tijek 16989 ▁istic 16976 謎 16972 잭 16959 ккей 16956 鏈 16954 θυπουργ 16952 猴 16949 蒋 16947 oraan 16944 र्तन 16944 ▁वेबसा 16934 ▁systems 16928 밭 16925 ▁દરમ 16922 웹 16917 翁 16916 臟 16916 ammet 16914 ▁weight 16911 esom 16909 嚇 16904 ==== 16903 愧 16900 恭 16898 ▁izbol 16897 ŏ 16896 ▁Lebo 16889 셰 16886 諮 16884 ispi 16878 ▁ନିର୍ବା 16872 ęzy 16865 헨 16859 😥 16853 厘 16847 òmasyon 16836 哇 16836 👌 16836 稻 16825 सन् 16823 รั่ง 16819 峻 16818 ዒ 16816 亨 16808 هره 16805 ድረግ 16786 ▁υποχ 16783 ▁hjæl 16777 よろ 16769 ↔ 16767 িজ্য 16765 铝 16762 ▁резу 16761 ▁getting 16759 bardziej 16757 ▁umiejęt 16755 विधा 16742 ኵ 16738 厄 16721 สะเก 16718 阁 16717 ▁testing 16716 ̋ 16703 ▁diou 16700 ▁مهند 16697 кипе 16695 หลด 16694 hatian 16682 ▁איינגעשרי 16680 alborg 16676 ebabkan 16673 कपा 16669 ້າພະ 16662 ▁ಪ್ರಶ 16659 팽 16658 銭 16653 ಓ 16645 砍 16645 ▁ευκαι 16641 esome 16635 撼 16624 ังกฤษ 16619 نال 16613 ៉ាង 16608 য়েছে 16601 ፬ 16600 wikk 16598 ▁chall 16591 ▁серп 16588 ▁provided 16587 隙 16587 lossing 16580 ceed 16579 তিহ 16578 煎 16577 ərrüfat 16573 ▁hÉ 16566 矯 16558 ▁peker 16556 ▁insurance 16548 킴 16546 utebol 16527 傍 16526 ंबई 16521 ▁توس 16518 արաբաղ 16515 rooms 16513 decyd 16507 nagy 16506 勉 16505 husayithi 16488 屠 16487 鹰 16477 ▁ඉදිරිප 16475 iglio 16473 厕 16472 좀 16469 temps 16465 իտասարդ 16453 هيون 16452 ▁Ws 16448 ▁gebru 16446 ▁اظ 16446 ▁მეგ 16443 꿔 16443 ▁ejem 16442 ▁даль 16435 ميس 16433 íncia 16430 逗 16425 陥 16421 꿈 16420 ▁Bangk 16419 ಲ್ಲಿಸ 16417 ▁inside 16407 נומקס 16398 愁 16396 ♦ 16395 וישן 16390 ඕ 16390 ▁गुज 16389 熙 16383 Ď 16366 摊 16357 ង្ស័យ 16352 ▁δουλει 16352 湧 16350 ▁Änd 16349 तिम 16344 攜 16344 қылы 16342 ▁উদ্ধ 16338 洒 16338 deň 16336 畜 16309 돋 16308 ▁неј 16307 哼 16306 າຊົນ 16304 ▁موب 16304 masti 16302 اثر 16297 넌 16294 ្ឆ 16293 uniden 16283 潭 16282 흰 16281 宰 16279 ▁окви 16276 澄 16275 ାର୍ଟ 16272 ▁мэн 16266 स्थ्य 16265 ಋ 16254 เกี่ยว 16251 besondere 16247 ▁projects 16245 sulot 16228 ▁benot 16226 沾 16226 ристову 16213 ೊಳ್ಳ 16212 ▁ରାଶି 16202 ▁below 16197 ೀಚ 16194 琪 16194 鹽 16191 彦 16188 ጺ 16183 klade 16180 褒 16180 饼 16173 ۓ 16172 ម្ពុ 16169 遲 16169 ▁piece 16159 겹 16158 ▁stuff 16157 繫 16156 ▁obb 16153 浄 16141 ► 16132 膏 16123 mušo 16115 喪 16113 窃 16110 툴 16106 ਦੁ 16101 쿼 16091 ▁ደግ 16090 遞 16087 ▁awesome 16086 巢 16084 ▁संशो 16081 ▁זייַ 16080 ▁career 16079 म्मी 16077 ▁makes 16070 鸿 16067 ທານ 16063 дования 16061 भिन्न 16055 ▁হার্ড 16048 塵 16031 eshimiwa 16029 ▁প্রত 16025 ▁хуули 16020 瀬 16020 ന്ത്രി 16016 睁 16016 滾 16013 颠 15988 ▁policies 15987 櫻 15969 筑 15965 ▁қауі 15964 ▁دانشجوی 15963 manse 15944 떨 15944 ▁ВМ 15927 ▁məqs 15926 сипе 15920 کتر 15919 莲 15903 idential 15896 ▁ਕਾਂਗਰ 15896 ▁aizs 15893 庸 15891 ▁निर्ण 15889 獸 15888 愣 15884 ▁rooms 15882 통령 15873 ǐ 15869 缅 15869 ▁vamm 15868 濯 15862 ▁happen 15860 邁 15857 ▁came 15852 ▁ଭୁବନ 15849 냅 15848 デザ 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15494 ▁coming 15494 აზრ 15492 थम 15481 TUK 15474 迴 15465 ogail 15464 ▁Θεσσα 15457 ▁મુખ 15455 툰 15452 यास 15448 鑽 15447 ahịa 15437 綿 15429 hetra 15423 潟 15422 夷 15414 纤 15408 тиже 15406 zout 15403 үмәте 15402 卜 15387 ીએ 15386 ไตล์ 15383 嘆 15383 ayıt 15375 撕 15365 ท่อง 15363 繕 15360 딘 15357 憧 15355 ▁ନିର୍ମ 15354 ▁देउ 15353 刮 15348 비스 15346 ughter 15339 agatan 15337 niaus 15334 숍 15332 칩 15330 adakan 15329 ▁Երեւ 15322 늄 15322 esai 15303 ͡ 15298 وجد 15289 噪 15287 irukan 15285 juangan 15281 olloin 15279 腎 15277 ◦ 15276 ▁ground 15270 ▁Banglad 15268 pausada 15257 ▁भवि 15254 爹 15254 wuregwu 15249 գել 15245 umentacion 15243 ॐ 15243 ▁Gipuzko 15243 คโนโลยี 15241 ▁upon 15232 ▁players 15230 vsem 15226 kanye 15223 əbər 15216 贤 15216 szön 15214 ething 15213 әмәғәт 15208 窝 15205 ▁someone 15198 hangi 15194 ទួល 15187 낳 15186 巩 15185 ອັງກິດ 15183 狮 15178 שריבן 15175 椒 15172 けれ 15161 Ĩ 15159 ivent 15154 侣 15152 ▁สิน 15151 ▁gouvern 15149 ▁kén 15145 իտաս 15144 澎 15139 脖 15137 ਡੀਓ 15129 纺 15125 譽 15115 ନାୟକ 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封? 幼@ 怀@ 厅A 貨A 悉B 灵B 甲C 摩C 層D ̃D ☆E 웃E 洛F 隨F 염G 웹G 亞H 乱H 紀I 폐I 診J 励J 邦K 旦K 奶L 荷L 脚M 违M 唯N 册N 앙O ─O 杀P 꾸P 雅Q 圈Q 遍R 津R 鼓S 複S ỘT ሯT 薬U 阅U 峰V 풀V 糖W 돈W 몇X 顯X 왜Y ぱY 략Z 률Z 跳[ ၵ[ ဉ\ ዥ\ ቨ] 틀] 旧^ 伦^ 启_ Ū_ 팔` 渡` ඬa ጪa 읽b 냐b 덕c 唱c 塞d ±d 娘e ጳe 勉f 잠f 톡g 쉬g 默h 夠h 鲜i 陽i 審j 汉j 惊k 宇k 須l 엇l 殺m 벽m 錯n 抵n ፒo 컬o 麗p 셨p 赤q 宮q 舒r 農r 懂s ဌs 媽t ഛt 顿u ゼu 굴v 迫v 逃w 緊w 潮x 伯x ”y 爾y 摄z 耐z 搜{ 豪{ 稱| 杂| 핵} 舉} ៕~ ぁ~ ઞ 偏 ਔ 욱 Ủ 逆 姆 讀 眠 ぼ 헤 労 ฺ 巧 삭 悩 链 押 订 図 紙 帝 嗎 賽 误 辆 ▪ 錢 株 抽 監 ⠀ 큼 妇 寶 Ή Ң 궁 顧 Ľ 藝 树 幫 滑 솔 汇 뜻 渐 辛 융 驚 災 딩 扩 Ố ˈ 刑 杨 伸 団 寫 돼 敏 页 魅 横 歯 鮮 沉 曼 歷 污 荣 択 ሟ ¬ ▼ ᅲ 袋 殊 慣 Ψ 释 笔 밖 坦 贷 井 雙 픽 ଃ 寒 慧 좌 Ở 仲 織 侵 턴 蒙 添 丈 寻 압 泉 刷 盖 乘 狗 죄 ۀ 擇 Ę 즉 삶 败 陸 诚 絶 ሾ 绿 饭 貴 蘭 焦 範 ጹ 泛 彻 掛 萨 ґ ൌ 隔 맨 Ў 빌 炎 溫 毫 狀 奈 啦 沿 総 났 캠 帯 ٣ 伟 御 跨 혀 菲 뮤 踏 丝 ऋ 惑 君 忍 싱 깨 圣 訳 潜 敬 損 敵 兄 词 婦 ヶ 臺 旁 ሹ 衛 迅 贫 遺 戻 截 淡 勒 둘 ੧ 斷 纷 聊 롯 秒 盘 剛 亦 聖 阪 庫 圳 归 兼 衡 羅 览 僅 坏 億 ٩ 랙 털 両 乾 叶 屬 꽃 恶 룹 떨 Ậ ๐ 빈 珍 弹 械 얻 薄 壁 贝 꼭 赢 – 抜 欠 疲 筆 顶 覆 齢 旗 甘 헌 섹 엘 枚 豆 ΐ 켜 遗 獨 肥 偶 訴 嘴 浴 ♦ 祖 킹 丹 濟 悲 棒 晴 咨 슨 ሦ 董 싸 搞 睛 坛 訂 凡 猫 므 尾 歲 奏 ঈ 孕 筋 鲁 鱼 ۃ 競 胡 脂 ŕ 歴 瓦 龍 ၠ 맥 腦 ೯ 魚 岡 拒 敗 Ơ 즘 ፦ 邀 戴 軟 냥 壓 窗 팬 曜 푸 癌 銷 錄 ✔ ፁ 狂 邮 ৷ ೩ 禮 慮 饮 ૅ 黃 頑 ‚! 꿈! 燃" ಛ" 봐# 腹# 摘$ 붙$ 嬉% 裏% ϋ& 퓨& 菌' 칼' ጢ( 豊( 執) ኗ) Ճ* 齐* 춘+ 柔+ 絕, 劃, 嘉- 净- 唐. 贴. 扱/ ၌/ 徐0 胸0 옥1 虚1 ጮ2 珠2 隐3 ೫3 ၢ4 坡4 Ķ5 튼5 †6 怖6 阵7 奋7 驾8 Ќ8 桌9 ঐ9 拜: 倫: 呈; ѕ; 磨< 沟< 储= 슴= 셀> 밤> 퇴? 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جلالهجمجمحجمىجميجىجيححجحجيحمحمىحميحىحيخخجخحخمخىخيدذذٰررسولرٰریالزسسجسجحسجىسحسحجسخسخىسخيسرسمسمجسمحسممسهسىسيششجشجيشحشحمشحيشخشرشمشمخشممشهشىشيصصحصححصحيصخصرصلعمصلىصلى الله عليه 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UTF-8 import sentencepiece as spm import jieba langs_ID = {'zh': 128301, 'ko': 128302, 'vi': 128303, 'de': 128317, 'en': 128318, 'nl': 128132, 'ms': 128109, 'id': 128110, 'tl': 128111, 'mn': 128103, 'my': 128104, 'th': 128105, 'lo': 128106, 'km': 128107, 'lt': 128112, 'et': 128113, 'lv': 128133, 'hu': 128115, 'pl': 128116, 'cs': 128117, 'sk': 128118, 'sl': 128119, 'hr': 128120, 'bs': 128121, 'sr': 128306, 'bg': 128304, 'mk': 128122, 'ru': 128305, 'uk': 128307, 'be': 128123, 'sq': 128124, 'el': 128125, 'ka': 128126, 'hy': 128127, 'ro': 128108, 'fr': 128100, 'es': 128102, 'pt': 128101, 'fa': 128310, 'he': 128311, 'ar': 128308, 'ps': 128309, 'tr': 128128, 'kk': 128129, 'uz': 128130, 'az': 128131, 'hi': 128315, 'ta': 128316, 'ur': 128313, 'bn': 128312, 'si': 128314, 'ne': 128114} translate_ID = 128300 class SpmTokenizer(object): def __init__(self, model_file): self.sp = spm.SentencePieceProcessor(model_file=model_file) self.specialIDNum = 300 self.eod_id = self.vocab_size - 1 self.eot_id = self.vocab_size - 2 self.pad_id = self.vocab_size - 3 # langsList=['ar','bg','bs','cs','de','el','en','es','et','fa', # 'fr','he','hr','hu','id','it','nl','pl','pt','ru', # 'sl','tr','ur'] # self.connectTxtToId = {} # i = 1 # for lang in langsList: # self.connectTxtToId[f'zh-{lang}'] = i # i += 1 # self.connectTxtToId[f'{lang}-zh'] = i # i += 1 @property def vocab_size(self): return self.sp.vocab_size() + self.specialIDNum @property def spmVocabSize(self): return self.sp.vocab_size() @property def eod(self): return self.eod_id def tokenize(self, text): """ Tokenize a string. """ return self.sp.encode(text) # # Adapted parallel corpus # texts = text.split(' _☪☣_ ') # if len(texts) == 1: # return self.sp.encode(text) # if len(texts) == 3: # ids1 = self.sp.encode(texts[0]) # connectId = self.sp.vocab_size() + self.connectTxtToId[texts[1]] # ids2 = self.sp.encode(texts[2]) # return ids1 + [connectId] + ids2 # return [] def convert_tokens_to_ids(self, tokens): return tokens def convert_ids_to_tokens(self, ids): ids = [id if id < self.sp.vocab_size() else 0 for id in ids] return self.decode(ids) def encode(self, text): res = self.tokenize(text) return res def decode(self, tokens): text = self.sp.decode(tokens) text = text.replace('\u2583', '\n') return text if __name__ == '__main__': Hindi_text = 'उत्साह बढ़ाने वाली कविताप्रेरणा दायक कविताप्रेरणा देने वाली कविताप्रेरणादायक कविता बच्चों के लिएप्रेरणादायक गजलप्रेरणादायक शायरी इन हिंदीप्रेरणादायक सुविचारप्रेरणादायक हिन्दी कविताप्रेरणादायी कविता हिंदीमनोबल बढ़ाने वाली कवितामनोबल बढ़ाने वाले विचारसकारात्मक कवितायेसकारात्मक सुविचारसकारात्मक सोच पर कविताहौसला पर कविताहौसला पर शायरीहौसला बढ़ाने वाली कविताहौसला बढ़ाने वाले विचारहौसला बढ़ाने वाले सुविचारज़िंदादिल ज़िन्दगी कविताज़िन्दगी पर कविता' Chinese_text = '湖人 板\n凳双枪乔丹-法玛尔和萨沙-武贾西奇回访斯台普斯，面对旧东家他们肯定想有所表现。第二节的故事是香农-布朗PK武贾西奇，奥多姆PK汉弗里斯，都是有故事的人。汉弗里斯篮下自投自抢拿下2分，别看情场对付卡戴珊的本领不如人，可到了球场上可不输奥多姆。小布朗单挑萨沙得手一次就没了声息，但湖人的整体进攻却哑火，篮网打的没多少章法，但依然扩大了领先优势，奥多姆没人防守的快攻上篮都不进，湖人问题不小。' Urdu_text = 'حماسه‌آفرینی جدید بسیجیان سلحشور در برگزاری رزمایش سراسری اقتدار عاشورایی بسیج تحت عنوان سپاهیان حضرت محمد رسول الله (ص) که یادآور اعزام غرورآفرین سپاهیان حضرت محمد رسول‌الله در دوران دفاع مقدس است، برگ زرین دیگری در کارنامه درخشان بسیج است که با حضور ده‌ها هزار نیرو بسیجی فداکار و داوطلب در عرصه‌های مختلف دفاعی، امنیتی، خدمات‌رسانی،‌محرومیت‌زدایی، بهداشتی و درمانی و غیره جلوه‌ای از همدلی، ایمان، قدرت و عمل انقلابی را به نمایش گذاشت.' Thai_text = 'ใน VDO ชุดนี้นะครับเราจะมาพูดถึง Amibroker Backtest นะ Backtest คืออะไร Backtest คือการทดสอบระบบของเรานะครับ ระบบซื้อ-ขาย ว่าระบบซื้อ-ขายของเราเนี่ยทำงานได้ดีขนาดไหน ทำกำไรให้เราขนาดไหน หรือว่าทำให้เราเจ๊งขนาดไหนนะครับ เพราะฉะนั้นเนี่ยเราจะมาดูในส่วนนี้กัน คราวนี้เดี๋ยวเปิด Amibroker ขึ้นมาก่อนนะครับโดยที่ในส่วนของ Backtest เนี่ย หลักๆมีส่วนประกอบอะไรบ้าง มาที่ปุ่มบวกนี่นะครับเพิ่ม windows นะครับ เห็นมั้ยครับแล้วเลื่อนลงมาในส่วนของ Backtest เนี่ยจะประกอบด้วย 2 ส่วนหลักๆคือส่วนของ Analysis document นี่นะครับเป็นการตั้งค่าว่าจะ test จากวันไหนถึงวันไหน จะ test หุ้นอะไรบ้าง ลองเปิดขึ้นมาดูแล้วกันนี่นะครับ Analysis ก็จะเห็นว่ามี Backtest อยู่ตรงนี้แล้วก็จะ test ตามอะไร ตามสูตรในนี้ อันนี้ก็เป็นสูตรที่ผมเขียนค้างไว้นะครับอันนี้ไม่เป็นไร เราก็จะ test กับหุ้นอะไรบ้างเนี่ย ก็บอกว่า test หุ้นทั้งหมดนะครับโดย test ในช่วงไหนบ้างนี่นะครับก็จะมีให้ set ได้ว่า test วันล่าสุดหรือ test bar ล่าสุดนี่นะครับ อันนี้คือ test จากวันไหน from ถึง to นะครับแล้วก็ตั้งค่าได้ในนี้ว่าจะเอาเป็นวันที่เท่าไร หรือพิมพ์ก็ได้นี่นะครับ พิมพ์เข้าไปของผมนี่เป็น format ของอังกฤษนะครับก็คือจะเป็น เดือน/วัน/ปีถ้าท่านใช้ format เป็นไทย จะขึ้นเป็น วัน/เดือน/ปี นะครับแล้วแต่ว่า windows คิดยังไงอันนี้เข้าใจตรงกันนะครับ คราวนี้สมมุติผมพิมพ์เข้าไปผมเปลี่ยนเป็น2012 เห็นมั้ยครับ ก็เปลี่ยนเดือนของผมตรงนี้แต่เดือนของท่านอาจอยู่ตรงนี้ก็ได้นะครับแล้วแต่ format ผมเปลี่ยนกลับแล้วกัน 2011 อันนี้ก็เป็นส่วนของ Analysis ต่อไปถ้าจะต้องมีส่วนของ windows ด้วย อ้าวส่วนของ formula ด้วยว่าท่านจะเขียนเงื่อนไขในการซื้อ ขายอย่างไรก็กด บวกนะครับแล้วก็บอก new formula คราวนี้จะมี windows โผล่ขึ้นมา มาโผล่อีกหน้าต่างนึง อ่ะอันนี้ก็เป็น windows ของผมเห็นมั้ยครับก็เป็น windows ที่เอาไว้เขียน formula โดยที่ formula ที่ท่านเขียนขึ้นมาเองเนี่ยมันจะไม่ไปรวมกับ default ของ Amibroker อันนี้เป็น default นะครับ เดี๋ยวปิดเข้าไปส่วน code formula ที่ผมเขียนขึ้นมาเนี่ย มันจะไปอยู่ในส่วนของ customs นี่นะครับ ผมก็มีเขียนอะไรทิ้งไว้นะครับก็ว่ากันไป อันนี้ก็ให้ทำความเข้าใจนะครับว่าในส่วนของ backtest เนี่ยประกอบด้วย 2 ส่วนหลักๆก็คือ new Analysis … Analysis document หรือ Formula 2 ส่วนนี้นะครับเดี๋ยวเราจะมาพูดถึง 2 ส่วนนี้กัน ว่าไปทีละส่วน ทีนี้ในส่วนของ backtest เนี่ยเป็นส่วนที่ยากที่สุดไม่ว่าจะเป็น level ของ introduction เป็น level basic advance หรือ inter media ก็ตามเพราะงั้นจะใช้เวลาในส่วนนี้เยอะสุด Ok นะครับ' Malay_text = 'ചായ കുടിയും പത്രം വായനയും \n കഴിഞ്ഞ ഹൈദ്രോസിക്കായുടെ ഒപ്പം സുലമാനിക്കയും എഴുന്നേറ്റു. പതുക്കെ കൂടെ നടന്ന് അമേരിക്കന്‍ വിശേഷങ്ങള്‍ എടുത്തിട്ടങ്ങലക്കി. അതോടെ ഹൈദ്രോസിക്ക കോടീശ്വരനില്‍ ചോദ്യം ചോദിക്കുന്ന സുരേഷ് ഗോപിയുടെ വീറും വാശിയോടും കൂടെ ചോദ്യങ്ങള്‍ ഓപഷനോടു കൂടിയും ഇല്ലാതെയും വീശി തുടങ്ങി. മണിച്ചിത്രത്താഴില്‍ നാഗവല്ലിയെ ഡാന്‍സ് ചെയ്ത് കൊണ്ടു പോകുന്നത് പോലെ ചോദ്യങ്ങള്‍ക്കുത്തരങ്ങളും കൊടുത്ത് കഥകളും പറഞ്ഞ് സുലൈമാനിക്ക മിഷന്‍ സക്സസ് ആക്കി. പാടവരമ്പിലെത്തി പാടമെല്ലാം കണ്ടിട്ടും ഹൈദ്രോസിക്കാന്റെ കരിനാക്കില്‍ നിന്നൊന്നും വരുന്നില്ല. അവസാനം സുലൈമാനിക്ക തന്നെ വിഷയം എടുത്തിട്ടു.' Arabic_text = 'با توجه به آنچه گفته شد، میدان مطالعاتی مرزها در ایران با محدودیت‌های اندیشگی، رشته‌ای و نهادی گسترده‌ای همراه بوده است. بیشتر این مطالعات محصور در حوزه‌ی جغرافیای سیاسی، انتظامی، بین‌المللی و سیاسیِ صرف بوده است. این در حالی است که مرزهای سیاسی در ایران، به‌لحاظ فرهنگی مرزهای گشوده‌ای هستند که نشان از فضای گسترده‌ی ایران فرهنگی دارند. بر این اساس، مرز بیشتر از آن‌که به‌معنای امتناع باشد، فضایی حیاتی و زیسته‌ی زیست-جهان‌ها و فرهنگ‌هایی است که حیات و چالش‌های آن‌ها موضوعاتی ملی است.' tokenizer = SpmTokenizer('spm.128k.model.1') tokens = tokenizer.tokenize(Chinese_text) ids = tokenizer.convert_tokens_to_ids(tokens) txt = tokenizer.convert_ids_to_tokens(ids) line1 = '34' line2 = '4434' a = f"{line1} _☪☣_ {'zh'}-{'ar'} _☪☣_ {line2}" b = tokenizer.tokenize(a) aa = '使 妇女 更 容易 感染 艾滋病毒 的 原因 还 包括 受 教育 机会 不 平等 ， 其中 包括 全面 的 性 教育 和 艾滋病毒 防治 教育 ， 很难 获得 固定收入 和 就业 ， 缺乏 经济 安全 ， 以及 暴力 和 恐惧 。' tokens2 = tokenizer.tokenize(aa) tokens2 = [i for i in tokens2 if i != 119132] tokens3 = tokenizer.tokenize(''.join(aa.split())) tokens3 = [i for i in tokens3 if i != 119132] for i in tokens2: if i != 119132: print(tokenizer.convert_ids_to_tokens([i])) for i in tokens3: print(tokenizer.convert_ids_to_tokens([i])) aaa = ' '.join(jieba.cut(''.join(aa.split()).strip())) print(txt) pass pcl_pangu/pcl_pangu/tokenizer/tokenization_jieba.py000066400000000000000000000046451443434453700232560ustar00rootroot00000000000000from io import open import os,sys TOKENIZER = None class JIEBATokenizer(object): def __init__(self, vocab_file, max_len=None): import sentencepiece as spm self.max_len = max_len if max_len is not None else int(1e12) # self.encoder = json.load(open(vocab_file)) model_file = vocab_file + ".model" vocab_file = vocab_file + ".vocab" f = open(vocab_file,'r', encoding='utf-8') lines = f.readlines() self.encoder = {} for line in enumerate(lines): key = line[1].split('\t')[0] self.encoder[key] = line[0] self.decoder = {v:k for k,v in self.encoder.items()} self.sp = spm.SentencePieceProcessor(model_file=model_file) self.translator = str.maketrans(" \n", "\u2582\u2583") self.eod_id = self.encoder[''] self.eot_id = self.encoder[''] self.pad_id = self.encoder[''] self.unk = 0 @property def vocab_size(self): return len(self.encoder) def __len__(self): return len(self.encoder) + len(self.special_tokens) @property def eod(self): return self.eot_id def tokenize(self, text): """ Tokenize a string. """ import jieba seg_list = [x.translate(self.translator) for x in jieba.cut(text, cut_all=False)] new_seg = " ".join(seg_list) return self.sp.encode(new_seg) def convert_tokens_to_ids(self, tokens): return tokens def convert_ids_to_tokens(self, ids): return self.decode(ids) def detokenize(self, token_ids): return self.decode(token_ids) def encode(self, text): res = self.tokenize(text) return res def decode(self, tokens): text = self.sp.decode(tokens) text = text.replace(' ', '').replace('\u2582', ' ').replace('\u2583', '\n') return text def get_tokenizer(): global TOKENIZER current_dir = os.path.dirname(os.path.dirname(__file__)) vocab = os.path.join(current_dir+'/tokenizer/bpe_4w_pcl/vocab') if TOKENIZER is None: TOKENIZER = JIEBATokenizer(vocab_file=vocab) return TOKENIZER if __name__ == '__main__': ids = [26, 80, 373, 5440, 2527, 1663, 11, 1268, 4896, 7902] vocab_file = '/userhome/pclproject/gpt/Megatron-LM-1.1-Pangu/megatron/tokenizer/bpe_4w_pcl/vocab' tokenizer = JIEBATokenizer(vocab_file) print(tokenizer.convert_ids_to_tokens(ids)) passpcl_pangu/setup.py000066400000000000000000000035361443434453700145620ustar00rootroot00000000000000# -*- coding: utf-8 -*- from setuptools import setup package_dir = \ {'': '.'} packages = \ ['pcl_pangu', 'pcl_pangu.context', 'pcl_pangu.dataset', 'pcl_pangu.model', 'pcl_pangu.model_converter', 'pcl_pangu.model.alpha', 'pcl_pangu.model.evolution', 'pcl_pangu.model.mPangu', 'pcl_pangu.tokenizer', 'pcl_pangu.tokenizer.bpe_4w_pcl', 'pcl_pangu.tokenizer.spm_13w', 'pcl_pangu.model.panguAlpha_pytorch', 'pcl_pangu.model.panguAlpha_pytorch.megatron', 'pcl_pangu.model.panguAlpha_pytorch.megatron.data', 'pcl_pangu.model.panguAlpha_pytorch.megatron.fp16', 'pcl_pangu.model.panguAlpha_pytorch.megatron.fused_kernels', 'pcl_pangu.model.panguAlpha_pytorch.megatron.model', 'pcl_pangu.model.panguAlpha_pytorch.megatron.mpu', 'pcl_pangu.model.panguAlpha_pytorch.megatron.tokenizer', 'pcl_pangu.model.panguAlpha_pytorch.tools', 'pcl_pangu.model.panguAlpha_mindspore', 'pcl_pangu.model.panguAlpha_mindspore.src', 'pcl_pangu.model_converter', 'pcl_pangu.online', 'pcl_pangu.online.infer', 'pcl_pangu.online.modelinfo', 'pcl_pangu.onnx_inference', 'pcl_pangu.onnx_inference.pangu', ] package_data = \ {'': ['*']} install_requires = \ ['numpy>=1.17.5,<2.0.0', 'sentencepiece>=0.1.0', 'jieba>=0.32.0', 'loguru>=0.3.0'] extras_require = \ {':python_version < "3.7"': ['dataclasses==0.6']} setup_kwargs = { 'name': 'pcl_pangu', 'version': '1.2.6.3', 'description': 'pcl_pangu', 'long_description': '# pcl_pangu ', 'author': '2022 PCL', 'author_email': 'pcl.openi@pcl.ac.cn', 'maintainer': None, 'maintainer_email': None, 'url': 'https://openi.pcl.ac.cn/PCL-Platform.Intelligence/pcl_pangu', 'package_dir': package_dir, 'packages': packages, 'package_data': package_data, 'install_requires': install_requires, 'extras_require': extras_require, 'python_requires': '>=3.6.2,<4.0.0', } setup(**setup_kwargs) pcl_pangu/test/000077500000000000000000000000001443434453700140205ustar00rootroot00000000000000pcl_pangu/test/alpha.py000066400000000000000000000011121443434453700154520ustar00rootroot00000000000000from pcl_pangu.context import set_context from pcl_pangu.model import alpha if __name__ == '__main__': set_context(backend='pytorch') data_path = '/home/yands/dataset/bin-train/bin-traintext_document' ckpt = '/home/yands/tmp/pangu-alpha_350m_fp16' config = alpha.model_config_gpu(model='350M',load=ckpt) alpha.inference(config,input='四川的省会是?') # # config = alpha.alpha_config_gpu(data_path=data_path) # alpha.train(config) config = alpha.model_config_gpu(data_path=data_path,load=ckpt) alpha.fine_tune(config) print() passpcl_pangu/test/alpha_fine_tune_openi_npu.py000066400000000000000000000164431443434453700215770ustar00rootroot00000000000000# install pcl_pangu import json import os os.system('python -m pip install pcl_pangu -i https://pypi.tuna.tsinghua.edu.cn/simple') import argparse from pcl_pangu.context import set_context from pcl_pangu.model import alpha import moxing as mox import time WORKROOT = None ### Defines whether the task is a training environment or a debugging environment ### def WorkEnvironment(environment): global WORKROOT if environment == 'train': workroot = '/home/work/user-job-dir' elif environment == 'debug': workroot = '/home/ma-user/work' print('current work mode:' + environment + ', workroot:' + workroot) WORKROOT = workroot return workroot def MultiObsToEnv(multi_data_url, workroot): for each_ckpt_file_url in multi_data_url: try: mox.file.copy_parallel(each_ckpt_file_url, workroot) print("Successfully Download {} to {}".format(each_ckpt_file_url, workroot)) except Exception as e: print('moxing download {} to {} failed: '.format( each_ckpt_file_url, workroot) + str(e)) return def setting_openi_cache_env_fromOBS(cache_ckpt_saving_dir = '/cache/ckpts', cache_strategy_saving_dir = '/cache/strategy_ckpt', cache_dataset_saving_dir = '/cache/dataset', ): mindrecord_file_num = 0 ckpt_url_list = [] dataset_url_list = [] strategy_url_list = [] if not os.path.exists(cache_ckpt_saving_dir): os.mkdir(cache_ckpt_saving_dir) if not os.path.exists(cache_dataset_saving_dir): os.mkdir(cache_dataset_saving_dir) if not os.path.exists(cache_strategy_saving_dir): os.mkdir(cache_strategy_saving_dir) multi_data_url_json = json.loads(multi_data_url) for item in multi_data_url_json: if '鹏程·盘古_alpha_2.6B预训练模型_4parts_ckpts_with_3embeddings' in item['dataset_name']: ckpt_url_list.append(item['dataset_url']) elif '鹏程pangu_alpha_2.6B_512_ckpt_strategy' in item['dataset_name']: strategy_url_list.append(item['dataset_url']) else: dataset_url_list.append(item['dataset_url']) ## unzip or tar the pretrain_ckpt_files # step 1: copying obs_files to /cache/ckpts/ MultiObsToEnv(ckpt_url_list, cache_ckpt_saving_dir) MultiObsToEnv(dataset_url_list, cache_dataset_saving_dir) MultiObsToEnv(strategy_url_list, cache_strategy_saving_dir) print(os.listdir(cache_ckpt_saving_dir)) print(os.listdir(cache_dataset_saving_dir)) print(os.listdir(cache_strategy_saving_dir)) ####################### rename the embedding.npys ########################## for item in os.listdir(cache_ckpt_saving_dir): this_file = os.path.join(cache_ckpt_saving_dir, item) rename_npys_list = ['position_embedding.npy', 'top_query_embedding.npy', 'word_embedding.npy'] for rename_npy in rename_npys_list: if rename_npy in item: os.system('mv {} {}'.format( this_file, os.path.join(cache_ckpt_saving_dir, rename_npy))) ############################################################################# print("setting env success.") # 下载模型文件结束后，写一个文件来表示下载成功 f = open("/cache/download_ckpt.txt", 'w') f.close() if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--model', default='2B6', type=str, choices=['350M', '2B6', '13B'], help="setting model size from ['350M', '2B6', '13B'], \n" "'350M' mp=1;\n" "'2B6' mp=2\n" "'13B' mp=8") parser.add_argument('--device_target', default='Ascend', type=str, choices=['Ascend'], help="setting device_type with ['Ascend']") parser.add_argument('--data_url', default='/tmp/dataset/text_document', type=str, help="setting mindrecord dataset from: '/cache/dataset' or from os.env('data_url').") parser.add_argument('--multi_data_url', help='path to multi dataset', default=WorkEnvironment('train')) parser.add_argument('--load', default='/tmp/dataset/', type=str, help="loading pretrained model ckpt, from: '/tmp/dataset'.") parser.add_argument('--train_url', default='/tmp/output/', type=str, help="save your model to: '/tmp/output' or to os.env('train_url').") parser.add_argument('--train_iters', default=40, type=int, help=">>> training iters setting by your Own >>>") args = parser.parse_args() set_context(backend='mindspore') #print(args) # local_model_saving_dir = os.path.join(WORKROOT, 'model') local_model_saving_dir = os.path.join("/cache/", 'model') os.makedirs(local_model_saving_dir, exist_ok=True) multi_data_url = args.multi_data_url cache_ckpt_saving_dir = '/cache/ckpts' cache_strategy_saving_dir = '/cache/strategy_ckpt' cache_dataset_saving_dir = '/cache/dataset' ###################### using one-node to do FilesCopying ############################# rank_id_str = os.getenv('RANK_ID', '0') rank_id = int( rank_id_str[rank_id_str.rfind('-') + 1:]) # 'RANK_ID': 'job24535502-job-facereidtome-hn-0/1' local_rank = rank_id if local_rank % 8 == 0: setting_openi_cache_env_fromOBS(cache_ckpt_saving_dir=cache_ckpt_saving_dir, cache_strategy_saving_dir=cache_strategy_saving_dir, cache_dataset_saving_dir=cache_dataset_saving_dir) while not os.path.exists("/cache/download_ckpt.txt"): time.sleep(1) ######################################################################################## model = args.model load = cache_ckpt_saving_dir data_path = cache_dataset_saving_dir save = local_model_saving_dir tmp = os.listdir(cache_strategy_saving_dir) if len(tmp) == 1: strategy_file_path = os.path.join(cache_strategy_saving_dir, tmp[0]) else: strategy_file_path = None #### 训练或微调350M模型至少需要1卡； # 2B6模型至少需要2卡；13B模型至少需要4/8卡，选择model='2B6'时，pcl_pangu已预制mp，使用多计算卡资源 config = alpha.model_config_npu(model=model, load=load, save=save, strategy_load_ckpt_path=strategy_file_path, data_path=data_path, batch_size=8, train_iters=args.train_iters, # manual setting, for 1K-iters, 2V100 may need 2-3hours to complete finetune save_checkpoint_steps=int(args.train_iters/2), # manual setting with save_iters, every X steps save model once finetune=True) alpha.fine_tune(config) passpcl_pangu/test/alpha_inference_oneCard_openi_npu.py000066400000000000000000000154731443434453700232160ustar00rootroot00000000000000# install pcl_pangu import json import os os.system('python -m pip install pcl_pangu -i https://pypi.python.org/simple/') import argparse from pcl_pangu.context import set_context from pcl_pangu.model import alpha import moxing as mox import time WORKROOT = None ### Defines whether the task is a training environment or a debugging environment ### def WorkEnvironment(environment): global WORKROOT if environment == 'train': workroot = '/home/work/user-job-dir' elif environment == 'debug': workroot = '/home/ma-user/work' print('current work mode:' + environment + ', workroot:' + workroot) WORKROOT = workroot return workroot def MultiObsToEnv(multi_data_url, workroot): for each_ckpt_file_url in multi_data_url: try: mox.file.copy_parallel(each_ckpt_file_url, workroot) print("Successfully Download {} to {}".format(each_ckpt_file_url, workroot)) except Exception as e: print('moxing download {} to {} failed: '.format( each_ckpt_file_url, workroot) + str(e)) return def setting_openi_cache_env_fromOBS(cache_ckpt_saving_dir = '/cache/ckpts', cache_strategy_saving_dir = '/cache/strategy_ckpt', cache_dataset_saving_dir = '/cache/dataset', ): mindrecord_file_num = 0 ckpt_url_list = [] dataset_url_list = [] strategy_url_list = [] if not os.path.exists(cache_ckpt_saving_dir): os.mkdir(cache_ckpt_saving_dir) if not os.path.exists(cache_dataset_saving_dir): os.mkdir(cache_dataset_saving_dir) if not os.path.exists(cache_strategy_saving_dir): os.mkdir(cache_strategy_saving_dir) multi_data_url_json = json.loads(multi_data_url) for item in multi_data_url_json: if 'mindrecord' in item['dataset_name']: mindrecord_file_num += 1 assert mindrecord_file_num <= 1, "Multi Mindrecord dataset not support now! please raise issue in 'https://openi.pcl.ac.cn/PCL-Platform.Intelligence/pcl_pangu'!" dataset_url_list.append(item['dataset_url']) elif 'strategy' in item['dataset_name']: strategy_url_list.append(item['dataset_url']) else: ckpt_url_list.append(item['dataset_url']) ## unzip or tar the pretrain_ckpt_files # step 1: copying obs_files to /cache/ckpts/ MultiObsToEnv(ckpt_url_list, cache_ckpt_saving_dir) MultiObsToEnv(dataset_url_list, cache_dataset_saving_dir) MultiObsToEnv(strategy_url_list, cache_strategy_saving_dir) print(os.listdir(cache_ckpt_saving_dir)) print(os.listdir(cache_dataset_saving_dir)) print(os.listdir(cache_strategy_saving_dir)) ####################### rename the embedding.npys ########################## for item in os.listdir(cache_ckpt_saving_dir): this_file = os.path.join(cache_ckpt_saving_dir, item) rename_npys_list = ['position_embedding.npy', 'top_query_embedding.npy', 'word_embedding.npy'] for rename_npy in rename_npys_list: if rename_npy in item: os.system('mv {} {}'.format( this_file, os.path.join(cache_ckpt_saving_dir, rename_npy))) ############################################################################# print("setting env success.") # 下载模型文件结束后，写一个文件来表示下载成功 f = open("/cache/download_ckpt.txt", 'w') f.close() if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--model', default='2B6', type=str, choices=['350M', '2B6', '13B'], help="setting model size from ['350M', '2B6', '13B'], \n" "'350M' mp=1;\n" "'2B6' mp=2\n" "'13B' mp=8") parser.add_argument('--device_target', default='Ascend', type=str, choices=['Ascend'], help="setting device_type with ['Ascend']") parser.add_argument('--data_url', default='/tmp/dataset/text_document', type=str, help="setting mindrecord dataset from: '/cache/dataset' or from os.env('data_url').") parser.add_argument('--multi_data_url', help='path to multi dataset', default=WorkEnvironment('train')) parser.add_argument('--load', default='/tmp/dataset/', type=str, help="loading pretrained model ckpt, from: '/tmp/dataset'.") parser.add_argument('--train_url', default='/tmp/output/', type=str, help="save your model to: '/tmp/output' or to os.env('train_url').") args = parser.parse_args() set_context(backend='mindspore') #print(args) local_model_saving_dir = os.path.join(WORKROOT, 'model') multi_data_url = args.multi_data_url cache_ckpt_saving_dir = '/cache/ckpts' cache_strategy_saving_dir = '/cache/strategy_ckpt' cache_dataset_saving_dir = '/cache/dataset' ###################### using one-node to do FilesCopying ############################# rank_id_str = os.getenv('RANK_ID', '0') rank_id = int( rank_id_str[rank_id_str.rfind('-') + 1:]) # 'RANK_ID': 'job24535502-job-facereidtome-hn-0/1' local_rank = rank_id if local_rank % 8 == 0: setting_openi_cache_env_fromOBS(cache_ckpt_saving_dir=cache_ckpt_saving_dir, cache_strategy_saving_dir=cache_strategy_saving_dir, cache_dataset_saving_dir=cache_dataset_saving_dir) while not os.path.exists("/cache/download_ckpt.txt"): time.sleep(1) ######################################################################################## model = args.model load = cache_ckpt_saving_dir data_path = cache_dataset_saving_dir save = local_model_saving_dir tmp = os.listdir(cache_strategy_saving_dir) if len(tmp) == 1: strategy_file_path = os.path.join(cache_strategy_saving_dir, tmp[0]) else: strategy_file_path = None #### 训练或微调350M模型至少需要1卡； # 2B6模型至少需要2卡；13B模型至少需要4/8卡，选择model='2B6'时，pcl_pangu已预制mp，使用多计算卡资源 config = alpha.model_config_npu(model=model, load=load, save=save, strategy_load_ckpt_path=strategy_file_path, data_path=data_path, finetune=True) alpha.inference(config, input='四川的省会是?') passpcl_pangu/test/alpha_online_requests.py000066400000000000000000000010111443434453700207470ustar00rootroot00000000000000import os os.system('python -m pip install pcl_pangu -i https://pypi.python.org/simple/') from pcl_pangu.online import Infer if __name__ == '__main__': prompt_input = '我先去吃饭了' model = "pangu-alpha-13B-md" # top_k = 0 # top_p = 0.9 # max_token = 50 # result_response = Infer.generate(model, prompt_input, max_token, top_k, top_p) # print(result_response) api_key = 'xxx' result_response = Infer.generate(model, prompt_input, api_key=api_key) print(result_response) pcl_pangu/test/alpha_openi_gpu.py000066400000000000000000000030111443434453700175170ustar00rootroot00000000000000# install pcl_pangu import os os.system('python -m pip install pcl_pangu') os.system('ls /tmp/dataset') os.system('nvidia-smi') import argparse from pcl_pangu.context import set_context from pcl_pangu.model import alpha if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--model', default='350M', type=str, choices=['350M', '2B6', '13B'], help="setting model size from ['350M', '2B6', '13B']") parser.add_argument('--data_url', default='/tmp/dataset/text_document', type=str, help="setting bin dataset text_document from: '/tmp/dataset'.") parser.add_argument('--load', default='/tmp/dataset/', type=str, help="loading pretrained model ckpt, from: '/tmp/dataset'.") parser.add_argument('--train_url', default='/tmp/output/', type=str, help="save your model to: '/tmp/output'.") args = parser.parse_args() set_context(backend='pytorch') print(args) model = args.model data_path = args.data_url load = args.load save = args.train_url config = alpha.model_config_gpu(model=model, load=load) alpha.inference(config, input='四川的省会是?') # # config = alpha.alpha_config_gpu(data_path=data_path) # alpha.train(config) config = alpha.model_config_gpu(data_path=data_path, load=load, save=save) alpha.fine_tune(config) print() passpcl_pangu/test/evolution.py000066400000000000000000000005331443434453700164170ustar00rootroot00000000000000from pcl_pangu.context import set_context from pcl_pangu.model import evolution if __name__ == '__main__': set_context(backend='pytorch') data_path = '/home/yizx/panguAlpha_pytorch/bin_max/CFT/cfttext_document' config = evolution.model_config_gpu(data_path=data_path) #alpha.inference(config) evolution.train(config) pass pcl_pangu/test/evolution_private.py000066400000000000000000000013311443434453700201460ustar00rootroot00000000000000import os import sys sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__)))) from pcl_pangu.context import set_context from pcl_pangu.model import evolution if __name__ == '__main__': set_context(backend='pytorch') data_path = '/home/yizx/panguAlpha_pytorch/bin_max/CFT/cfttext_document' # config = evolution.model_config_gpu(data_path=data_path) # evolution.train(config) config = evolution.model_config_gpu(model='2B6', load='/userhome/pangu/model/continuous/pangu_fp16_8mp_2b6_continuous_multask052_1016_1/merged/pangu-alpha-evolution_2.6b_fp16/') evolution.inference(config, input="文本分类：\n基本上可以说是诈骗\n选项：积极，消极\n答案：") pass pcl_pangu/test/infer_int8.py000066400000000000000000000017631443434453700164460ustar00rootroot00000000000000from pcl_pangu.context import set_context from pcl_pangu.model import alpha from pcl_pangu.model import evolution if __name__ == '__main__': set_context(backend='onnx-cpu') data_path = '/home/yands/dataset/bin-train/bin-traintext_document' ckpt = '/home/yands/tmp/models/onnx_int8_pangu_evolution' ckpt = '/home/yands/tmp/models/onnx_int8_pangu_alpha_13b' ckpt = '/home/yands/tmp/models/onnx_int8_pangu_alpha_2b6' config = alpha.model_config_onnx(model='2B6',load=ckpt) alpha.inference(config,input='四川的省会是?') ckpt = '/home/yands/tmp/models/onnx_int8_pangu_evolution' config = evolution.model_config_onnx(model='2B6',load=ckpt) evolution.inference(config,input='四川的省会是?') evolution.inference(config,input='四川的省会是?') # # config = alpha.alpha_config_gpu(data_path=data_path) # alpha.train(config) # config = alpha.model_config_gpu(data_path=data_path,load=ckpt) # alpha.fine_tune(config) print() passpcl_pangu/test/mPangu_inference_oneCard_openi_npu.py000066400000000000000000000155441443434453700233570ustar00rootroot00000000000000# install pcl_pangu import json import os os.system('python -m pip install pcl_pangu -i https://pypi.python.org/simple/') import argparse from pcl_pangu.context import set_context from pcl_pangu.model import mPangu import moxing as mox import time WORKROOT = None ### Defines whether the task is a training environment or a debugging environment ### def WorkEnvironment(environment): global WORKROOT if environment == 'train': workroot = '/home/work/user-job-dir' elif environment == 'debug': workroot = '/home/ma-user/work' print('current work mode:' + environment + ', workroot:' + workroot) WORKROOT = workroot return workroot def MultiObsToEnv(multi_data_url, workroot): for each_ckpt_file_url in multi_data_url: try: mox.file.copy_parallel(each_ckpt_file_url, workroot) print("Successfully Download {} to {}".format(each_ckpt_file_url, workroot)) except Exception as e: print('moxing download {} to {} failed: '.format( each_ckpt_file_url, workroot) + str(e)) return def setting_openi_cache_env_fromOBS(cache_ckpt_saving_dir = '/cache/ckpts', cache_strategy_saving_dir = '/cache/strategy_ckpt', cache_dataset_saving_dir = '/cache/dataset', ): mindrecord_file_num = 0 ckpt_url_list = [] dataset_url_list = [] strategy_url_list = [] if not os.path.exists(cache_ckpt_saving_dir): os.mkdir(cache_ckpt_saving_dir) if not os.path.exists(cache_dataset_saving_dir): os.mkdir(cache_dataset_saving_dir) if not os.path.exists(cache_strategy_saving_dir): os.mkdir(cache_strategy_saving_dir) multi_data_url_json = json.loads(multi_data_url) for item in multi_data_url_json: if 'mindrecord' in item['dataset_name']: mindrecord_file_num += 1 assert mindrecord_file_num <= 1, "Multi Mindrecord dataset not support now! please raise issue in 'https://openi.pcl.ac.cn/PCL-Platform.Intelligence/pcl_pangu'!" dataset_url_list.append(item['dataset_url']) elif 'strategy' in item['dataset_name']: strategy_url_list.append(item['dataset_url']) else: ckpt_url_list.append(item['dataset_url']) ## unzip or tar the pretrain_ckpt_files # step 1: copying obs_files to /cache/ckpts/ MultiObsToEnv(ckpt_url_list, cache_ckpt_saving_dir) MultiObsToEnv(dataset_url_list, cache_dataset_saving_dir) MultiObsToEnv(strategy_url_list, cache_strategy_saving_dir) print(os.listdir(cache_ckpt_saving_dir)) print(os.listdir(cache_dataset_saving_dir)) print(os.listdir(cache_strategy_saving_dir)) ####################### rename the embedding.npys ########################## for item in os.listdir(cache_ckpt_saving_dir): this_file = os.path.join(cache_ckpt_saving_dir, item) rename_npys_list = ['position_embedding.npy', 'top_query_embedding.npy', 'word_embedding.npy'] for rename_npy in rename_npys_list: if rename_npy in item: os.system('mv {} {}'.format( this_file, os.path.join(cache_ckpt_saving_dir, rename_npy))) ############################################################################# print("setting env success.") # 下载模型文件结束后，写一个文件来表示下载成功 f = open("/cache/download_ckpt.txt", 'w') f.close() if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--model', default='2B6', type=str, choices=['350M', '2B6', '13B'], help="setting model size from ['350M', '2B6', '13B'], \n" "'350M' mp=1;\n" "'2B6' mp=2\n" "'13B' mp=8") parser.add_argument('--device_target', default='Ascend', type=str, choices=['Ascend'], help="setting device_type with ['Ascend']") parser.add_argument('--data_url', default='/tmp/dataset/text_document', type=str, help="setting mindrecord dataset from: '/cache/dataset' or from os.env('data_url').") parser.add_argument('--multi_data_url', help='path to multi dataset', default=WorkEnvironment('train')) parser.add_argument('--load', default='/tmp/dataset/', type=str, help="loading pretrained model ckpt, from: '/tmp/dataset'.") parser.add_argument('--train_url', default='/tmp/output/', type=str, help="save your model to: '/tmp/output' or to os.env('train_url').") args = parser.parse_args() set_context(backend='mindspore') #print(args) local_model_saving_dir = os.path.join(WORKROOT, 'model') multi_data_url = args.multi_data_url cache_ckpt_saving_dir = '/cache/ckpts' cache_strategy_saving_dir = '/cache/strategy_ckpt' cache_dataset_saving_dir = '/cache/dataset' ###################### using one-node to do FilesCopying ############################# rank_id_str = os.getenv('RANK_ID', '0') rank_id = int( rank_id_str[rank_id_str.rfind('-') + 1:]) # 'RANK_ID': 'job24535502-job-facereidtome-hn-0/1' local_rank = rank_id if local_rank % 8 == 0: setting_openi_cache_env_fromOBS(cache_ckpt_saving_dir=cache_ckpt_saving_dir, cache_strategy_saving_dir=cache_strategy_saving_dir, cache_dataset_saving_dir=cache_dataset_saving_dir) while not os.path.exists("/cache/download_ckpt.txt"): time.sleep(1) ######################################################################################## model = args.model load = cache_ckpt_saving_dir data_path = cache_dataset_saving_dir save = local_model_saving_dir tmp = os.listdir(cache_strategy_saving_dir) if len(tmp) == 1: strategy_file_path = os.path.join(cache_strategy_saving_dir, tmp[0]) else: strategy_file_path = None #### 训练或微调350M模型至少需要1卡； # 2B6模型至少需要2卡；13B模型至少需要4/8卡，选择model='2B6'时，pcl_pangu已预制mp，使用多计算卡资源 config = mPangu.model_config_npu(model=model, load=load, save=save, strategy_load_ckpt_path=strategy_file_path, data_path=data_path, finetune=True) mPangu.inference(config, input='四川的省会是?', src_language='zh', tag_language='kk') passpcl_pangu/test/model_info_test.py000066400000000000000000000004751443434453700175520ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2022/8/12 # @Author: pcl from pcl_pangu.pcl_pangu.online import ModelInfo if __name__ == '__main__': models = ModelInfo.model_list() print(f"models: {models}") model = ModelInfo.model_info(model="pangu-alpha-13B-md") print(f"model: {model}") pcl_pangu/test/online_infer_test.py000066400000000000000000000010161443434453700200760ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2022/8/17 # @Author: pcl import os os.system('python -m pip install pcl_pangu -i https://pypi.python.org/simple/') from pcl_pangu.online import Infer if __name__ == '__main__': model = "pangu-alpha-13B-md" model = "pangu-alpha-evolution-2B6-pt" prompt_input = "文本分类：\n基本上可以说是诈骗\n选项：积极，消极\n答案：" api_key = "****" result = Infer.generate(model, prompt_input, api_key) print(f"result: {result}")

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جلالهجمجمحجمىجميجىجيححجحجيحمحمىحميحىحيخخجخحخمخىخيدذذٰررسولرٰریالزسسجسجحسجىسحسحجسخسخىسخيسرسمسمجسمحسممسهسىسيششجشجيشحشحمشحيشخشرشمشمخشممشهشىشيصصحصححصحيصخصرصلعمصلىصلى الله عليه 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-10.1681 营 -10.1682 ▁深刻 -10.1682 ▁啥 -10.1683 ▁信心 -10.1686 ▁夹 -10.1686 ▁咬 -10.17 创 -10.1702 ▁简 -10.1703 ▁轮 -10.1703 ▁货币 -10.1704 ▁地下 -10.1706 ▁坚定 -10.1707 众 -10.1708 ▁Sm -10.171 ▁一大 -10.1711 轩 -10.1713 冰 -10.1714 ▁一人 -10.1717 ▁观 -10.1717 ▁账号 -10.1719 裤 -10.1721 ▁经理 -10.1721 ▁逐步 -10.1727 ▁舒服 -10.1731 ▁一套 -10.1732 ▁领先 -10.1733 ▁差不多 -10.1733 ▁必然 -10.1735 ▁亲自 -10.1737 槸 -10.1746 冷 -10.175 备 -10.1757 ▁机器人 -10.1757 ▁一时 -10.1761 潮 -10.1783 ▁灯 -10.1785 ▁画面 -10.1788 ▁引发 -10.1788 ▁冲击 -10.179 ▁采访 -10.1792 ▁秘密 -10.1792 ▁通讯 -10.1794 ▁熟 -10.1794 p -10.1795 ▁不锈钢 -10.1795 ▁荣 -10.1807 ▁大大 -10.1811 ▁名称 -10.1814 ▁四川 -10.1815 ▁地球 -10.1826 ▁头发 -10.1827 ▁营业 -10.1827 罪 -10.1829 ▁截至 -10.183 ▁尺寸 -10.1831 ▁隐 -10.1832 ▁法院 -10.1837 ▁歌 -10.1838 ▁如下 -10.1847 ▁两位 -10.1855 ▁商务 -10.1859 ▁股东 -10.186 ▁黑暗 -10.1864 ▁厂商 -10.1865 ▁泰 -10.1868 ▁挡 -10.1874 ▁CO -10.1882 ▁利润 -10.1889 乡 -10.1893 ▁袁 -10.1895 ▁围绕 -10.1895 h -10.1895 ▁医 -10.1896 ▁一项 -10.1898 ▁不由 -10.1902 er -10.1902 ▁乌 -10.1904 ▁丹 -10.1909 ▁公子 -10.191 ▁总体 -10.1914 ▁市民 -10.1915 持 -10.1917 材料 -10.1919 ▁专利 -10.1921 ▁身影 -10.1921 ▁卫 -10.1926 ▁描述 -10.1927 ▁扑 -10.1927 ▁Er -10.1928 直 -10.193 方面 -10.1932 ▁亿美元 -10.1937 ▁留学 -10.1937 ▁停车 -10.1939 ▁内心 -10.1945 ▁一生 -10.1949 ▁替 -10.1953 ▁对外 -10.1957 ▁怀疑 -10.1958 ▁不然 -10.1958 x -10.196 唇 -10.196 棒 -10.1965 ▁护 -10.197 ▁广场 -10.1973 京 -10.1981 ▁殿 -10.1983 ▁胜 -10.1987 ▁遭 -10.199 沙 -10.1991 绝 -10.1996 泰 -10.2 烈 -10.2001 ▁便利 -10.2002 ▁這 -10.2008 ▁再说 -10.2008 ▁冯 -10.2014 ▁浓 -10.2018 像 -10.2021 末 -10.2022 ▁厨房 -10.2024 ▁公益 -10.2025 Y -10.2033 项目 -10.2033 ▁虚 -10.2033 ▁权益 -10.2038 ▁列 -10.2039 ▁环节 -10.204 环境 -10.2041 ▁链接 -10.2043 ▁QQ -10.2044 ▁考核 -10.2047 ▁学历 -10.2047 ▁私服 -10.205 ▁呆 -10.2052 ▁躲 -10.2053 ▁电商 -10.2057 ▁线上 -10.2061 ▁穿着 -10.2067 刚 -10.2069 ▁爆发 -10.2074 ▁铝 -10.2079 ▁监控 -10.208 凤 -10.2083 ▁没事 -10.2086 ▁伸手 -10.2087 ▁电力 -10.2092 ▁好奇 -10.2094 ▁论 -10.2096 ▁照顾 -10.2097 ▁一口气 -10.2097 险 -10.2101 ▁好看 -10.2101 ▁爬 -10.2104 厚 -10.2113 ▁反正 -10.2113 ▁尊重 -10.2115 ▁居 -10.2119 段时间 -10.2122 ▁驱动 -10.2124 金属 -10.2128 ▁暴 -10.2129 ▁室 -10.2129 叔 -10.2132 ▁公园 -10.2132 ▁自信 -10.2142 ▁兵 -10.2142 ▁凤 -10.2144 ▁霍 -10.2148 ▁一身 -10.2149 ▁业绩 -10.2151 ▁服装 -10.2153 藏 -10.2154 ▁狼 -10.2155 ▁高中 -10.2161 ▁of -10.2165 ▁每日 -10.2166 ▁三人 -10.2166 ▁一面 -10.2169 ▁配件 -10.2171 ▁原创 -10.2173 ▁便宜 -10.2181 露 -10.2183 ▁各自 -10.2184 ▁Pr -10.2186 罩 -10.2187 ▁优先 -10.2187 ▁标志 -10.2193 烟 -10.2195 ▁深化 -10.2201 杆 -10.2204 ▁威 -10.2204 ▁帝 -10.2206 ▁骂 -10.2207 ▁一半 -10.2207 ▁混合 -10.2208 ▁铺 -10.221 ▁上下 -10.2211 ▁略 -10.2211 屾 -10.2213 ▁隐私 -10.2215 患 -10.2217 ▁移 -10.2218 ▁静 -10.2219 ▁车型 -10.222 ▁信用 -10.2229 ▁适当 -10.2234 ▁动态 -10.2239 ▁隔 -10.224 ▁非法 -10.224 ▁尝试 -10.2242 ▁弱 -10.2243 ▁浣 -10.2245 ▁迎来 -10.2249 ▁应对 -10.2251 ▁维 -10.2255 ▁喂 -10.2257 ▁添加 -10.226 ▁标题 -10.2268 ▁小小 -10.2272 ▁紧紧 -10.2274 十 -10.229 ▁废 -10.2294 ▁瞧 -10.23 ▁认可 -10.2302 ▁辅助 -10.2304 ▁身份证 -10.2313 ▁分分 -10.2314 ▁累计 -10.2314 季度 -10.2318 ▁转移 -10.2322 ▁手上 -10.2326 ▁范 -10.2327 郎 -10.2328 ▁账户 -10.2334 涛 -10.2336 裂 -10.2339 ▁话题 -10.234 补 -10.234 ▁思维 -10.2341 ▁董事长 -10.2344 ▁青春 -10.2345 ▁竹 -10.2363 ▁信任 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-12.3931 ▁自考 -12.3933 枕 -12.3933 dd -12.3933 咳 -12.3934 方便 -12.3936 ▁姹 -12.3936 訊 -12.3936 hu -12.3937 ▁驻地 -12.3937 ▁CBA -12.3938 ▁新风 -12.394 呮 -12.394 ▁惰 -12.394 ure -12.3941 ▁希尔 -12.3942 ▁升华 -12.3942 ▁烤肉 -12.3943 ╂ -12.3943 ▁帷幕 -12.3943 ▁赞誉 -12.3945 肿瘤 -12.3946 喳 -12.3946 ▁武学 -12.3948 ▁追加 -12.395 ▁比尔 -12.395 緢 -12.3951 巨 -12.3952 ▁第十七 -12.3953 ans -12.3956 ▁皂 -12.3956 ▁洋溢 -12.3957 St -12.3957 ▁文凭 -12.396 ▁大字 -12.3962 ▁从根本 -12.3966 ▁颂 -12.3967 ▁同名 -12.3967 ▁贫血 -12.3968 難 -12.3969 ▁务工 -12.3969 ▁CA -12.397 ▁衡阳 -12.3972 net -12.3972 cr -12.3973 淨 -12.3974 ▁动画片 -12.3977 ▁吸毒 -12.3977 ▁专家学 -12.3977 ▁感官 -12.3978 ▁230 -12.3978 ▁特工 -12.3978 ▁更长 -12.3978 ▁恩怨 -12.3979 ▁更衣 -12.398 ▁兴业 -12.398 ▁防滑 -12.3981 ▁扩充 -12.3982 ▁最能 -12.3982 盯 -12.3983 橙 -12.3983 ▁短路 -12.3984 ▁点火 -12.3984 ▁儒家 -12.3984 梭 -12.3985 tu -12.3989 ▁Linux -12.3989 ▁品德 -12.399 巅 -12.3991 sy -12.3992 ▁钦 -12.3992 ▁不差 -12.3992 ≤ -12.3992 ▁柔声 -12.3993 恃 -12.3993 一亮 -12.3994 ▁狂热 -12.3999 ▁参议 -12.4 ▁化石 -12.4001 鑸 -12.4001 烬 -12.4002 ▁买来 -12.4004 ▁白金 -12.4006 ▁悉尼 -12.4006 眼圈 -12.4006 谎 -12.4006 ▁全国人大常委会 -12.4007 ▁农作 -12.4008 囱 -12.4009 ▁結構 -12.4009 ▁寒意 -12.401 ▁末日 -12.401 ▁老老 -12.4012 ▁乐视 -12.4013 ▁管事 -12.4014 ▁娑 -12.4016 ▁原生 -12.4016 ▁争执 -12.4016 ▁登出 -12.4017 ▁瞳 -12.4017 IT -12.4018 祭 -12.4018 ▁安阳 -12.4019 ▁雪地 -12.4019 ▁纳兰 -12.4019 煦 -12.402 1.0 -12.4022 ▁TA -12.4022 bi -12.4022 RI -12.4023 ▁朋 -12.4024 ▁破门 -12.4024 CT -12.4025 UT -12.4026 ╃ -12.4027 ▁盘算 -12.4028 ▁直营 -12.403 ▁车友 -12.4032 ▁麻痹 -12.4032 ▁白骨 -12.4034 ▁MO -12.4034 淇 -12.4035 ▁山中 -12.4036 ▁离合 -12.4036 ▁皮鞋 -12.4037 ▁CH -12.4039 ▁裘 -12.404 ▁负有 -12.404 刚落 -12.4042 腐败 -12.4044 怔 -12.4044 聞 -12.4045 ▁波斯 -12.4049 ▁iPad -12.4049 ▁Al -12.4049 进步 -12.4049 ▁加成 -12.405 ▁软硬 -12.405 ▁物理学 -12.405 ▁知己 -12.4052 ▁蓝光 -12.4052 ▁音樂 -12.4053 ▁报关 -12.4053 〗 -12.4053 钮 -12.4054 ▁推崇 -12.4056 ▁吃苦耐劳 -12.4056 ▁书架 -12.4057 ▁心智 -12.4058 ▁經過 -12.4058 ▁几只 -12.4058 ▁定下 -12.406 ▁群里 -12.4061 ▁有余 -12.4061 ▁美甲 -12.4066 ▁拖鞋 -12.4066 ▁公车 -12.4067 ▁筆 -12.4067 武帝 -12.4068 ▁破绽 -12.4069 ▁谦虚 -12.407 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-12.5198 ▁用尽 -12.5199 南京市 -12.5199 ▁Cookie -12.5202 ▁靓丽 -12.5202 ▁狠抓 -12.5202 ▁网约 -12.5203 ▁叱 -12.5204 ▁年版 -12.5204 进行 -12.5205 ▁芊 -12.5205 策划 -12.5208 ▁相隔 -12.521 ▁血统 -12.521 ▁浦东 -12.521 慶 -12.5211 釋 -12.5212 涚 -12.5212 ▁电光 -12.5213 ▁各路 -12.5215 纳斯 -12.5216 ▁联军 -12.5217 不掉 -12.5218 ▁贯通 -12.5218 ▁忐忑 -12.5218 ▁飲 -12.522 ▁侵袭 -12.522 ▁炕 -12.522 ▁接听 -12.5221 ▁属地 -12.5221 ▁燥 -12.5222 ▁冲到 -12.5223 粤 -12.5223 ▁201 -12.5223 ▁千秋 -12.5224 ▁嫣 -12.5225 多时 -12.5226 ▁剥离 -12.5227 ▁乳液 -12.5228 江湖 -12.5232 ▁闪避 -12.5233 ▁轻视 -12.5233 ▁初恋 -12.5233 ▁科技部 -12.5234 ▁首轮 -12.5236 ▁瞩目 -12.5237 ▁竞彩 -12.5239 ▁版主 -12.524 ▁PO -12.5241 ▁找人 -12.5243 ▁沃尔沃 -12.5244 ▁汤姆 -12.5244 ▁献给 -12.5245 ▁計 -12.5245 ▁PET -12.5246 ▁IBM -12.5247 ▁紧身 -12.5249 戮 -12.5249 ▁卦 -12.525 ▁惡 -12.5252 ▁石门 -12.5253 ▁乐山 -12.5254 ▁布衣 -12.5254 ▁多想 -12.5255 ▁余下 -12.5255 ▁魔幻 -12.5256 拉扯 -12.5258 饰品 -12.5258 ▁古巴 -12.5261 ▁排骨 -12.5264 ▁瘫痪 -12.5264 ▁多彩 -12.5265 ▁殴打 -12.5266 ▁凹凸 -12.5266 晓明 -12.5267 ▁计数 -12.5267 理解 -12.5267 ▁意見 -12.5267 女儿 -12.5268 ▁亢 -12.527 ▁捡起 -12.527 峥 -12.5271 ▁水肿 -12.5273 ▁同城 -12.5273 而上 -12.5274 ▁推断 -12.5274 ▁嬴 -12.5274 ▁谨防 -12.5275 ▁六七 -12.5275 ▁夙 -12.5276 肽 -12.5276 ▁遗留 -12.5276 ▁底薪 -12.5277 ≥ -12.5277 ▁揩 -12.5279 ▁SM -12.5281 ▁淡化 -12.5282 ID -12.5283 ▁PA -12.5284 ▁反腐 -12.5284 ▁妇产 -12.5284 肋 -12.5286 ▁武当 -12.5287 ▁榆 -12.529 花园 -12.529 ▁内核 -12.529 ▁考评 -12.5291 良好 -12.5291 ▁并列 -12.5291 .95 -12.5292 ▁跟我 -12.5294 針 -12.5295 ▁极乐 -12.5298 ▁發布 -12.5298 ▁三步 -12.53 前进 -12.53 ▁霹雳 -12.53 ▁海淀 -12.53 巫 -12.53 ▁去向 -12.5301 燒 -12.5302 ▁波及 -12.5303 ▁拉丁 -12.5305 ▁虚幻 -12.5305 ▁亚军 -12.5305 ▁肇庆 -12.5306 思路 -12.5309 ▁私营 -12.5311 ▁風險 -12.5311 婕 -12.5312 寄 -12.5313 ▁估价 -12.5315 旀 -12.5315 ▁房中 -12.5315 ▁单手 -12.5315 VE -12.5315 ▁劳累 -12.5316 驗 -12.5316 ▁灵性 -12.5316 ▁中所 -12.5317 ▁卫东 -12.5317 怯 -12.5317 ▁杨浩 -12.5318 ▁辦理 -12.5318 联播 -12.5318 ▁才刚 -12.5318 ▁学徒 -12.532 濋 -12.532 ▁还敢 -12.5321 ▁嚴 -12.5321 ▁史蒂 -12.5322 ▁从小到 -12.5322 ▁处份 -12.5322 ▁吸尘 -12.5323 蒸 -12.5323 ▁覺得 -12.5327 ▁苍蝇 -12.5327 枯 -12.5329 毒药 -12.5329 ▁電視 -12.533 ▁瞿 -12.533 ▁相等 -12.5331 ▁史诗 -12.5333 ▁189 -12.5335 ▁旧金山 -12.5337 ▁可得 -12.534 改造 -12.5342 ▁流下 -12.5343 ▁绒 -12.5344 ▁商报 -12.5346 ▁今朝 -12.5346 ▁七大 -12.5348 ▁2030 -12.5348 ▁疲倦 -12.5349 ▁震天 -12.5349 ▁盔 -12.5351 护航 -12.5353 ▁虔诚 -12.5353 ▁惊吓 -12.5354 ▁中国银行 -12.5355 ▁Ar -12.5357 ▁萧晨 -12.5357 ▁怎麼 -12.5358 ▁波浪 -12.5359 鏡 -12.5359 魰 -12.5359 ▁自查 -12.5361 龙女 -12.5363 ▁飞奔 -12.5363 ▁研發 -12.5364 浸 -12.5365 ▁造船 -12.5366 揭 -12.5366 ▁单调 -12.5368 ▁咪 -12.5369 ▁螃蟹 -12.537 ▁仇人 -12.5371 汶 -12.5372 ▁人要 -12.5372 阅读 -12.5372 ▁短片 -12.5373 ▁儿科 -12.5375 ▁腮 -12.5376 冤 -12.5377 ▁SH -12.538 ▁冰川 -12.538 量化 -12.538 ▁白皮 -12.538 米尔 -12.5381 庤 -12.5381 ▁液位 -12.5381 歆 -12.5384 ▁钾 -12.5384 ▁舍利 -12.5384 ▁空心 -12.5384 ▁港澳台 -12.5385 岋 -12.5386 ▁立委 -12.5386 ▁梭 -12.5387 ▁软软 -12.5388 茅 -12.5389 ▁应邀 -12.539 120 -12.539 ▁李明 -12.5392 ▁驚 -12.5392 ▁土工 -12.5393 ▁宣誓 -12.5394 ▁一文 -12.5395 时许 -12.5398 ▁挂断 -12.5398 ▁听证 -12.5399 丫 -12.54 ▁棱 -12.5401 ▁2.8 -12.5401 ▁圆圆 -12.5403 ▁尼斯 -12.5403 ▁生平 -12.5404 配合 -12.5405 ▁书信 -12.5407 ▁一个劲 -12.5408 ▁超碰 -12.5409 ▁军区 -12.5409 コ -12.5411 ▁居士 -12.5411 ▁热处理 -12.5412 ▁刪除 -12.5413 ▁天界 -12.5414 530 -12.5415 ▁捷克 -12.5416 ide -12.5416 纳河 -12.5417 ▁通州 -12.5418 崄 -12.5419 ▁从头到 -12.542 ▁滞后 -12.5421 ▁悲痛 -12.5421 ▁低低 -12.5422 保障 -12.5422 ▁进水 -12.5423 ▁元神 -12.5424 短信 -12.5426 ▁洽 -12.5426 ▁由衷 -12.5427 饺 -12.5429 ▁弱小 -12.543 ▁慵 -12.5432 ▁切身 -12.5434 本科 -12.5434 ▁西域 -12.5434 ▁坝 -12.5434 ▁增至 -12.5435 ▁懦 -12.5436 ▁罗汉 -12.5436 320 -12.5437 ▁走红 -12.5437 ▁欒 -12.5438 ▁写信 -12.5439 ▁绊 -12.544 ↑ -12.5441 ▁若能 -12.5442 騗 -12.5443 ▁坦白 -12.5444 ▁衙 -12.5445 ▁木马 -12.5446 ▁诚心 -12.5446 ▁启蒙 -12.5447 ▁马蹄 -12.5447 ▁杜承 -12.5448 ▁环保型 -12.5448 ▁盾牌 -12.5449 ▁五万 -12.545 墩 -12.5451 ▁青蛙 -12.5451 铏 -12.5452 ▁接班 -12.5455 溂 -12.5455 ▁瞬息 -12.5456 ▁卷帘 -12.5456 ▁天佑 -12.5457 ▁大清 -12.5458 ▁花儿 -12.5459 ▁退后 -12.5459 ▁咬着 -12.546 ste -12.5462 ▁满身 -12.5462 ▁奉行 -12.5462 ▁同居 -12.5462 ▁喝醉 -12.5463 ▁藥 -12.5464 ence -12.5465 蜒 -12.5466 ▁檢 -12.5466 ▁殿堂 -12.5467 ▁四点 -12.5467 ▁盐酸 -12.5467 ▁保税 -12.5468 ▁轻巧 -12.5469 ▁民政 -12.547 VA -12.5471 ▁洗发 -12.5472 后果 -12.5473 ▁概不 -12.5473 ▁或多 -12.5475 ▁海滨 -12.5476 ▁杉 -12.5477 ▁专车 -12.5482 ▁声势 -12.5482 ▁臉 -12.5483 ▁阿森纳 -12.5483 ▁车内 -12.5483 烷 -12.5483 ▁討論 -12.5484 ▁红颜 -12.5484 ▁丈 -12.5485 56 -12.5487 ▁天雷 -12.5487 ▁环卫 -12.5487 ▁电量 -12.5488 ▁SD -12.5489 ▁岚 -12.5489 ▁音量 -12.549 産 -12.5491 my -12.5491 ▁挑逗 -12.5491 ▁幻觉 -12.5492 ▁卫士 -12.5492 ▁裂纹 -12.5494 ▁农业部 -12.5495 屡 -12.5495 ▁临走 -12.5498 ▁la -12.5498 ▁远超 -12.5499 ▁贬 -12.5499 ▁重叠 -12.5501 霉素 -12.5501 ▁清冷 -12.5501 ▁零售价 -12.5501 ▁欣然 -12.5501 ▁183 -12.5501 ▁车床 -12.5502 ▁中要 -12.5503 按 -12.5503 ▁讀 -12.5505 ▁救出 -12.5506 ▁显露 -12.5508 ▁兴旺 -12.5508 ▁之子 -12.5509 ▁德育 -12.551 ▁通通 -12.551 ▁快到 -12.5511 ▁大马 -12.5512 ▁皖 -12.5514 ▁玩得 -12.5515 塑料 -12.5515 ▁催生 -12.5516 ▁快车 -12.5516 ▁玩儿 -12.5516 ▁建构 -12.5517 ▁几道 -12.5518 ▁林木 -12.5518 ▁NT -12.5518 同于 -12.5519 ns -12.5521 ▁收银 -12.5521 ▁刺痛 -12.5521 ▁补习 -12.5521 ▁特制 -12.5521 ▁煲 -12.5523 ▁护送 -12.5523 ▁变相 -12.5523 维斯 -12.5525 ▁禁毒 -12.5525 ▁担负 -12.5525 冒 -12.5525 ▁含税 -12.5525 ▁刻画 -12.5526 ▁深感 -12.5526 ▁新郎 -12.553 ▁口子 -12.553 ▁摆摆 -12.5531 ▁软化 -12.5532 ▁亲热 -12.5533 ▁确有 -12.5535 ran -12.5536 ▁泰州 -12.5537 84.23 -12.5537 鐵 -12.5538 松松 -12.5538 ▁席位 -12.5538 iz -12.5538 ▁危急 -12.5538 ▁呕 -12.5538 ▁壓 -12.5539 自治县 -12.5539 芊 -12.554 ▁陰 -12.5541 ▁皇城 -12.5542 ▁參考 -12.5544 ▁淫水 -12.5544 ▁尘土 -12.5544 ▁艳丽 -12.5544 茸 -12.5545 ▁平地 -12.5545 仪表 -12.5545 ▁表妹 -12.5545 ▁爱国主义 -12.5546 ▁选秀 -12.5546 ▁十来 -12.5546 ▁法语 -12.5547 ▁漠 -12.5548 mi -12.5548 ▁国美 -12.5549 ▁迷迷 -12.5551 ▁发泡 -12.5552 ▁石榴 -12.5552 日游 -12.5553 咎 -12.5553 ▁冊 -12.5553 晔 -12.5554 ▁除湿 -12.5554 ▁英勇 -12.5555 ▁安全事故 -12.5555 ▁多头 -12.5556 ▁小姨 -12.5557 ▁16774 -12.5558 ▁收下 -12.5559 ▁坍 -12.5559 黨 -12.556 ▁慷慨 -12.556 ▁提款 -12.556 ▁聊城 -12.5561 91 -12.5562 ▁不分 -12.5562 ▁集市 -12.5563 ▁过长 -12.5563 ▁天灾 -12.5567 ▁朴素 -12.5568 天盖 -12.5568 1.5 -12.5568 蚕 -12.5569 ▁时日 -12.5569 ▁厦门市 -12.5569 囩 -12.5571 博物馆 -12.5574 ▁ICP -12.5576 ▁磁盘 -12.5576 ▁匠心 -12.5577 ▁退伍 -12.5577 ▁战火 -12.5578 ▁纯真 -12.5582 ▁甜品 -12.5582 ▁碍 -12.5583 ▁存货 -12.5583 ▁来宾 -12.5584 IL -12.5584 ▁林立 -12.5585 ▁沼泽 -12.5587 ▁流云 -12.5588 舉 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▁撑起 -12.6467 ▁发源 -12.6467 ▁等离子 -12.6468 ▁榫 -12.6469 ▁前排 -12.647 ▁校准 -12.647 ▁日历 -12.647 ▁改變 -12.6471 羹 -12.6471 ▁通体 -12.6472 ▁黑色素 -12.6472 way -12.6473 ▁歌剧 -12.6473 ▁锋芒 -12.6473 适应 -12.6474 ▁非标 -12.6474 ▁彈 -12.6474 貴 -12.6475 ▁低保 -12.6476 ▁化疗 -12.6477 ▁拉链 -12.6477 叙 -12.6478 ▁vi -12.648 微信 -12.648 ▁per -12.6481 ▁婚庆 -12.6482 ▁王林 -12.6482 强大 -12.6482 ▁狀況 -12.6482 ▁足彩 -12.6483 辊 -12.6484 ▁意气 -12.6484 瞰 -12.6485 願 -12.6485 挟 -12.6486 ▁太低 -12.6488 ▁拜师 -12.6488 ▁絕 -12.6488 ▁因地制 -12.6488 ast -12.649 ▁自然科学 -12.6491 ▁提质 -12.6492 少侠 -12.6493 ▁地雷 -12.6493 ▁秀丽 -12.6494 ▁恶毒 -12.6494 ▁惦记 -12.6495 ▁褒 -12.6496 ▁受灾 -12.6497 ▁称得 -12.6498 ▁哪一 -12.6499 ▁大洋 -12.6499 ▁义工 -12.6499 評 -12.6499 ▁正气 -12.65 会员 -12.65 ▁辛辣 -12.65 ▁TD -12.6501 萃 -12.6501 ▁如风 -12.6502 ▁胭脂 -12.6503 ▁---- -12.6504 ▁心力 -12.6506 ▁京剧 -12.6506 正恩 -12.6507 ▁热忱 -12.6507 ▁令狐冲 -12.651 朔 -12.6512 令营 -12.6512 ▁求救 -12.6513 ▁裁量 -12.6514 ▁申博 -12.6514 沖 -12.6515 ▁觉察 -12.6515 ▁结交 -12.6516 ▁暂且 -12.6517 ▁蝉 -12.6518 ▁冒犯 -12.6518 ▁地底 -12.6518 祖国 -12.6519 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▁span -12.8274 ▁学着 -12.8274 ▁夏利 -12.8274 ▁魔气 -12.8274 ▁受让 -12.8275 ▁袁绍 -12.8275 ▁很早 -12.8275 拆 -12.8276 ▁结石 -12.8277 ▁ч -12.8278 MW -12.8279 ▁部門 -12.8279 钼 -12.828 ▁领航 -12.8281 ▁中风 -12.8281 ▁出生地 -12.8282 ▁敦促 -12.8282 ▁露营 -12.8282 ▁290 -12.8283 ▁隔天 -12.8283 视镜 -12.8284 ▁不绝 -12.8285 酰胺 -12.8285 ▁滑稽 -12.8286 ▁变身 -12.8286 ▁黑板 -12.8287 系統 -12.8289 ▁轻叹 -12.8289 ▁阴险 -12.8289 丽莎 -12.8289 禧 -12.829 ▁避难 -12.829 ▁木瓜 -12.829 ▁醒过 -12.8291 ▁容错 -12.8291 ▁輪 -12.8291 当当 -12.8292 解释 -12.8294 7.4 -12.8295 ▁伯格 -12.8295 ▁摇篮 -12.8296 ▁下级 -12.8296 ▁肖像 -12.8297 ▁145 -12.8297 out -12.8298 賞 -12.8299 ▁同质 -12.8299 ▁韶关 -12.8299 uc -12.8301 ▁鼻梁 -12.8303 ▁汶 -12.8303 ▁械 -12.8304 ▁多米 -12.8304 ▁野战 -12.8304 ▁健脾 -12.8308 ▁遮盖 -12.8308 ▁举个 -12.8308 ▁地市 -12.8309 ▁万美 -12.831 ▁台独 -12.831 eta -12.831 ▁km -12.831 峪 -12.8311 偉 -12.8314 lic -12.8314 ▁一脉 -12.8315 ▁巳 -12.8315 ▁着火 -12.8316 ▁洗护 -12.8318 鍕 -12.8318 ▁Internet -12.832 ▁羽翼 -12.832 und -12.8322 .66 -12.8325 ▁悄无 -12.8326 ▁鲨 -12.8327 ▁Su -12.8327 ▁蛙 -12.8327 ▁雷鸣 -12.8328 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▁台前 -13.1886 ▁階段 -13.1886 梅尔 -13.1887 ▁重合 -13.1887 ▁铃铛 -13.1888 ▁手袋 -13.1888 mar -13.1888 ▁二氧化 -13.1888 ▁地税 -13.189 ▁练兵 -13.189 ▁尸骨 -13.1891 ▁龙潭 -13.1891 ▁逆行 -13.1891 ▁機制 -13.1892 ▁古文 -13.1892 ▁再接再 -13.1892 之徒 -13.1893 4.8 -13.1893 ▁众将 -13.1893 湍 -13.1893 ▁經典 -13.1893 ▁危房 -13.1894 国务院 -13.1895 鲤 -13.1896 ▁春暖花 -13.1896 6.2 -13.1898 ▁成員 -13.1899 忿 -13.19 ▁车用 -13.19 跡 -13.19 ▁训练场 -13.19 ▁施政 -13.1901 490 -13.1901 ▁性病 -13.1902 挨 -13.1902 ▁運行 -13.1902 汞 -13.1903 ▁得不偿失 -13.1906 ▁供图 -13.1906 ▁相间 -13.1907 ▁依着 -13.1907 垰 -13.1908 ▁233 -13.1909 ▁假象 -13.1909 辫 -13.1911 ▁現象 -13.1911 ▁思忖 -13.1912 一凡 -13.1912 ▁摩托罗拉 -13.1912 荟 -13.1913 ▁九寨沟 -13.1915 ▁国际象棋 -13.1915 rp -13.1916 ▁三秒 -13.1918 不錯 -13.1918 ▁Office -13.1918 ▁胎盘 -13.1918 ▁走遍 -13.1919 悯 -13.1922 ▁澹台 -13.1923 ▁內部 -13.1923 如山 -13.1926 突出 -13.1926 ▁结肠 -13.1927 ▁Sun -13.1927 ▁云林 -13.1927 ▁萨克 -13.1927 ▁河畔 -13.1928 风寒 -13.1928 俑 -13.1929 悄悄 -13.1929 ▁尘世 -13.1929 ▁198 -13.1929 场次 -13.1929 ▁压铸 -13.1929 余悸 -13.193 段落 -13.193 掖 -13.193 结局 -13.1931 7.2 -13.1931 ▁献身 -13.1931 ▁条理 -13.1933 晚饭 -13.1934 ▁躲藏 -13.1934 ▁TXT -13.1934 ▁城东 -13.1935 凈 -13.1935 kk -13.1935 ▁农副 -13.1936 .70 -13.1937 ▁颓废 -13.1937 飞燕 -13.1937 ▁吱吱 -13.1939 Net -13.194 彝 -13.1941 ▁漂泊 -13.1942 ▁脖 -13.1942 ▁简简 -13.1943 ▁實性 -13.1946 ▁精兵 -13.1949 ▁生硬 -13.1949 GI -13.195 支队 -13.1951 ▁青丝 -13.1951 ▁延庆 -13.1952 ▁崎岖 -13.1953 ▁花旗 -13.1954 ▁腹胀 -13.1955 ▁爱看 -13.1958 ▁诸法 -13.196 ▁见得 -13.196 ▁装订 -13.196 ▁黑山 -13.1961 ▁取景 -13.1961 ▁科举 -13.1963 ▁達到 -13.1965 悠扬 -13.1965 ▁变大 -13.1965 ▁白内 -13.1966 ▁習慣 -13.1968 盂 -13.1968 425 -13.1968 ▁8.2 -13.1968 ▁收容 -13.1971 ▁吸音 -13.1971 455 -13.1972 大桥 -13.1973 ▁恼羞成 -13.1974 ▁除臭 -13.1975 ▁李佳 -13.1975 技術 -13.1975 920 -13.1975 ▁编导 -13.1976 ▁蝗 -13.1976 ▁国库 -13.1976 ▁城西 -13.1977 Ch -13.1977 榴 -13.1977 ▁漂移 -13.1977 貼 -13.198 ▁先给 -13.198 獨 -13.1981 ≡ -13.1981 ▁乌兰 -13.1981 ▁实况 -13.1982 軟 -13.1982 ▁拳脚 -13.1983 ▁绝情 -13.1984 ▁王八 -13.1984 ▁维稳 -13.1986 ▁有损 -13.1986 ors -13.1988 ▁真實 -13.1988 褂 -13.1988 礼品 -13.1989 850 -13.1989 ▁白龙 -13.199 氨 -13.1991 ▁皓月 -13.1991 ▁磨砂 -13.1991 ▁静坐 -13.1992 ▁龙井 -13.1993 ▁下唇 -13.1993 ▁病害 -13.1993 ▁淮北 -13.1995 ▁斗罗 -13.1997 ▁目不转 -13.1998 ▁一男一 -13.1998 ▁小萝莉 -13.1998 ▁一松 -13.1999 ▁球磨 -13.2 ▁麻花 -13.2 ▁哪吒 -13.2001 孫 -13.2002 宀 -13.2003 ▁損失 -13.2003 ▁除草 -13.2003 颍 -13.2005 ▁司马懿 -13.2005 ▁5500 -13.2005 ▁副驾驶 -13.2006 ▁大四 -13.2009 ▁阴雨 -13.2009 ▁云山 -13.201 ▁蛾 -13.2011 ▁功名 -13.2011 ▁酋长 -13.2011 飚 -13.2012 ▁取件 -13.2013 ▁细嫩 -13.2013 ▁龙腾 -13.2013 条不 -13.2014 生气 -13.2015 家电 -13.2015 ▁抿嘴 -13.2016 ▁白俄罗斯 -13.2017 ▁肉食 -13.2017 ▁改口 -13.2018 ▁南亚 -13.2018 ▁勃然 -13.2018 ▁微红 -13.202 ▁陆陆 -13.202 ▁衰减 -13.202 ▁消防车 -13.202 ▁贾跃亭 -13.2021 ▁米勒 -13.2021 隐私 -13.2022 ▁市容 -13.2023 ▁Mb -13.2024 ▁异口同 -13.2024 ▁苏瑾 -13.2025 ral -13.2026 ▁豆类 -13.2026 ▁婉转 -13.2027 ▁癫 -13.2027 ▁醫師 -13.2028 ▁掌教 -13.2028 ▁仁爱 -13.2029 ▁豪杰 -13.2029 ▁娇媚 -13.2029 ▁一塌糊 -13.203 ▁西证 -13.203 ▁结节 -13.2031 一波 -13.2031 ▁花盆 -13.2031 ▁黑水 -13.2032 ▁叶尘 -13.2032 ▁铁甲 -13.2035 区划 -13.2036 ▁惨淡 -13.2037 ▁依序 -13.2037 ▁稳中 -13.2037 ▁You -13.2037 ▁披肩 -13.2038 ▁显赫 -13.2038 践 -13.204 業者 -13.2041 ▁娄底 -13.2041 ▁随笔 -13.2041 ▁懵懂 -13.2043 万两 -13.2043 ▁制式 -13.2044 ▁狗屁 -13.2045 ▁草酸 -13.2047 ▁电影票 -13.2048 詋 -13.2048 ▁昼 -13.2048 ▁情急 -13.2049 ▁整流 -13.2049 TU -13.205 典型 -13.2051 304 -13.2051 拘 -13.2052 ▁六千 -13.2052 ▁氦 -13.2052 ▁松口 -13.2053 ▁意欲 -13.2054 锂 -13.2055 绞 -13.2057 ▁喜讯 -13.2058 ▁拘束 -13.206 ▁左上 -13.206 ▁聆 -13.2062 幸福 -13.2062 ▁进料 -13.2062 ▁造物 -13.2063 330 -13.2063 ▁擂 -13.2065 ▁声乐 -13.2065 外交部 -13.2067 玩具 -13.2067 ▁楹 -13.2068 ▁掣 -13.2073 ▁Fe -13.2074 ▁顽皮 -13.2075 ▁意犹未 -13.2075 ▁红莲 -13.2075 ▁定睛 -13.2076 ▁蒙蒙 -13.2076 ▁银山 -13.2076 ▁3200 -13.2076 米兰 -13.2076 粟 -13.2076 ▁辨析 -13.2076 訓 -13.2077 ▁時尚 -13.2077 光大 -13.2077 box -13.2078 祐 -13.2078 sf -13.2078 ▁助剂 -13.2079 ▁早产 -13.2079 裱 -13.208 浪费 -13.2082 风发 -13.2082 ▁兴隆 -13.2082 118 -13.2083 唆 -13.2083 ▁鲤 -13.2084 竻 -13.2084 ▁病毒感染 -13.2085 908 -13.2086 ▁储气 -13.2086 ▁成婚 -13.2086 牆 -13.2087 sch -13.2087 525 -13.2088 ▁成瘾 -13.2088 ▁半夏 -13.2088 ▁傲天 -13.2089 ▁传令 -13.2089 ▁难求 -13.209 ous -13.2091 ▁接住 -13.2091 屏幕 -13.2091 扈 -13.2092 ▁1954 -13.2093 ▁捕捞 -13.2093 志刚 -13.2093 ▁二零一 -13.2094 ▁2700 -13.2094 ▁肝胆 -13.2094 ▁非农 -13.2094 ▁一肖中 -13.2094 公室 -13.2096 ▁富民 -13.2097 ▁素颜 -13.2098 ▁瞪眼 -13.2099 ▁诵读 -13.21 益匪浅 -13.21 辘 -13.2101 ▁航站 -13.2103 1.1 -13.2103 ▁葫芦岛 -13.2104 ▁易购 -13.2104 ▁张杰 -13.2104 ▁火电 -13.2105 飛 -13.2105 ▁俘 -13.2106 ▁赠与 -13.2107 ▁伯恩 -13.2108 万计 -13.2109 ▁完好无 -13.211 ▁真够 -13.2112 ▁掘 -13.2113 高考 -13.2113 ▁紫气 -13.2114 ▁印第安 -13.2114 ▁萎 -13.2115 rea -13.2116 反射 -13.2117 ▁交了 -13.2117 ▁富家 -13.2119 699 -13.2119 蔷 -13.2119 ▁宜兴 -13.2119 ▁染发 -13.212 ▁南疆 -13.212 sen -13.212 纷纷 -13.2124 ▁闲话 -13.2124 蒙蒙 -13.2126 氰 -13.2127 ▁山间 -13.2128 ▁覆灭 -13.2128 ▁巴勒斯坦 -13.2129 ▁三叔 -13.2129 235 -13.213 独秀 -13.213 ▁威斯 -13.2131 ▁FB -13.2132 ▁偏重 -13.2132 ▁三段 -13.2133 ▁SW -13.2133 ▁少先队 -13.2134 ▁铂金 -13.2134 ▁五颜六 -13.2135 ▁全封闭 -13.2135 ▁炮火 -13.2135 ▁帝国主义 -13.2137 老四 -13.2137 ▁169 -13.2137 ▁脱手 -13.2137 喜事 -13.2138 ▁城下 -13.2138 ▁埋藏 -13.2139 而无 -13.214 党史 -13.214 men -13.2142 ▁沦落 -13.2142 ▁扎堆 -13.2142 ▁相接 -13.2142 ▁散装 -13.2143 ▁嚦 -13.2143 rad -13.2144 ▁陈云 -13.2144 ▁义气 -13.2144 ▁闯荡 -13.2145 ▁之功 -13.2145 ▁哗啦 -13.2147 ▁寄来 -13.2147 ▁云阳 -13.2148 老虎 -13.2148 ▁果不其 -13.2148 指南 -13.2149 ▁style -13.2151 heng -13.2151 ▁冷却水 -13.2151 ▁无明 -13.2152 桃源 -13.2152 UM -13.2154 视眈 -13.2154 ▁满口 -13.2155 ▁文化名 -13.2155 书店 -13.2155 ▁探亲 -13.2157 忻 -13.2158 ▁MPV -13.2158 ▁弹奏 -13.2159 ▁发狂 -13.2159 ▁萧条 -13.216 tom -13.216 ▁正以 -13.2161 YS -13.2161 ▁鹿晗 -13.2161 女生 -13.2163 ▁读过 -13.2165 ▁美麗 -13.2165 悅 -13.2166 ▁绢 -13.2166 倩倩 -13.2166 ▁RPG -13.2167 670 -13.2167 ▁驻马店 -13.2169 ▁门卫 -13.2169 ▁查尔斯 -13.217 烯 -13.2171 ▁承诺书 -13.2171 配比 -13.2171 ▁议价 -13.2172 ▁太深 -13.2174 ▁密钥 -13.2176 ▁寻宝 -13.2177 装周 -13.2177 ▁has -13.2177 ▁照常 -13.2177 ▁三联 -13.2178 ▁闪躲 -13.2178 ▁读音 -13.2179 亰 -13.2179 ▁硬朗 -13.2179 ▁泡茶 -13.218 par -13.218 白白 -13.218 ▁判别 -13.218 ▁流着 -13.2182 ▁无踪 -13.2182 銅 -13.2183 ▁水压 -13.2183 ▁碧玉 -13.2184 ▁告一 -13.2184 ▁槟榔 -13.2185 推荐 -13.2185 478 -13.2185 昼 -13.2187 ▁翱翔 -13.2187 ▁次第 -13.2188 ▁乔楠 -13.2189 一级 -13.2189 自负 -13.2191 凶恶 -13.2191 6.7 -13.2192 嗲 -13.2193 ▁炒菜 -13.2193 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-13.5075 ▁菱形 -13.5075 ▁居心 -13.5075 ▁ARM -13.5075 ▁佩奇 -13.5076 趙 -13.5076 ▁造影 -13.5077 ▁西昌 -13.5078 ▁崇明 -13.5079 ▁罢免 -13.5079 rin -13.5079 pal -13.5079 ▁长腿 -13.5083 ▁五洲 -13.5084 ▁敲诈 -13.5084 nor -13.5084 一夕 -13.5085 ▁腥味 -13.5086 913 -13.5086 ▁青鸾 -13.5087 狩 -13.5087 ▁谦逊 -13.5087 驯 -13.5087 审查 -13.5088 2.3 -13.5088 襲 -13.5088 ▁闭塞 -13.5089 ▁结转 -13.509 ▁消防队 -13.5091 ▁牌型 -13.5092 ▁贴息 -13.5092 ▁天秤 -13.5093 ▁奥拉 -13.5095 mil -13.5096 ▁第三十六 -13.5096 遢 -13.5096 916 -13.5098 ▁睡觉时 -13.5099 ▁傷害 -13.5099 簯 -13.51 ▁珠光 -13.5101 701 -13.5101 ▁体育用品 -13.5102 滋事 -13.5103 难免 -13.5103 ▁张弛 -13.5103 422 -13.5104 ▁墓葬 -13.5104 ▁倚天 -13.5106 ▁选聘 -13.5106 IQ -13.5108 ▁甩出 -13.5108 357 -13.5108 丰收 -13.5108 865 -13.5109 謀 -13.511 ▁正月初 -13.5111 ▁老僧 -13.5112 ▁兰博基 -13.5112 778 -13.5113 笃 -13.5113 惺 -13.5113 ▁西江 -13.5114 ▁开门见 -13.5115 ▁担负起 -13.5117 ▁炼金 -13.5117 暫 -13.5118 ▁打開 -13.5118 ▁竹叶 -13.5119 BR -13.5119 oth -13.5119 ▁读数 -13.5119 ▁军政 -13.5119 tory -13.5119 醬 -13.5121 ▁反潜 -13.5122 ▁仙王 -13.5124 ▁乔木 -13.5125 ▁墨绿 -13.5125 spec -13.5126 868 -13.5127 ump -13.5129 ▁消泡 -13.513 ▁Wind -13.513 ▁计程 -13.513 ▁施以 -13.513 ▁娇笑 -13.5131 ▁烧饼 -13.5131 ▁抚州 -13.5133 ▁继续加强 -13.5133 ▁姆巴 -13.5133 ▁山峦 -13.5134 ▁龙辰 -13.5134 ▁自欺 -13.5135 惩 -13.5135 ▁易碎 -13.5135 ▁刻录 -13.5135 ▁祝你 -13.5136 ▁血雾 -13.5137 ▁生津 -13.5137 ▁陈奕 -13.5138 ▁LPL -13.5139 萤 -13.514 ▁祝融 -13.5141 ▁绿意 -13.5142 ▁WEB -13.5142 基督 -13.5144 嗟 -13.5145 Mobile -13.5145 ▁单亲 -13.5145 ▁兴办 -13.5146 340 -13.5147 2016 -13.5147 ▁类药 -13.5148 ▁胸襟 -13.5148 999 -13.5148 ▁国务委员 -13.515 ▁根深蒂 -13.5151 噸 -13.5151 ▁胃部 -13.5152 漸 -13.5154 ▁语重心 -13.5154 ▁售中 -13.5154 〝 -13.5155 ▁娇声 -13.5155 ▁家俱 -13.5159 擀 -13.5159 不挂 -13.516 ▁連接 -13.516 ▁進步 -13.5161 ache -13.5161 ▁米粒 -13.5163 吊胆 -13.5164 ▁延吉 -13.5165 ▁洛离 -13.5165 ▁约瑟夫 -13.5166 ▁几杯 -13.5167 ▁刘枫 -13.5167 ▁存管 -13.5167 ▁united -13.517 ▁国产品牌 -13.517 ▁总领事 -13.5171 ▁颠覆性 -13.5172 货币基金 -13.5172 一刻 -13.5172 ▁李昊 -13.5173 aj -13.5173 UC -13.5176 磺 -13.5176 1.9 -13.5177 罗列 -13.5178 ▁指標 -13.518 差异 -13.5181 齐齐 -13.5184 祎 -13.5184 ▁艳阳 -13.5186 徕 -13.5186 ▁红霞 -13.5187 ridge -13.5187 ▁收跌 -13.5187 ▁OPEC -13.5187 ▁嗓门 -13.5188 呷 -13.5188 很 -13.5189 ▁锯齿 -13.5189 ration -13.519 ▁皮炎 -13.5191 完整 -13.5191 步枪 -13.5192 ▁关进 -13.5192 吴 -13.5192 ▁不同寻常 -13.5193 ▁汗毛 -13.5193 呛 -13.5194 核心 -13.5194 ▁法兰西 -13.5194 璜 -13.5195 渡河 -13.5195 plus -13.5196 ▁朝野 -13.5198 ▁汽水 -13.5198 佯 -13.5199 ▁三门峡 -13.5199 ▁莫急 -13.5199 ▁拼凑 -13.5199 ▁脚掌 -13.5199 ▁MSCI -13.52 bao -13.5201 ▁铜川 -13.5201 Service -13.5202 ▁扣留 -13.5202 ▁商標 -13.5203 ▁1933 -13.5203 蜃 -13.5204 杳 -13.5206 ▁龚瑞妮 -13.5207 ▁人山人 -13.5208 ▁水煮 -13.5208 ▁面红耳 -13.5208 ▁包揽 -13.5208 ▁从无到 -13.5209 198 -13.5209 ▁喷气 -13.5209 ▁致谢 -13.521 ▁南朝 -13.5211 划一 -13.5213 ▁發出 -13.5214 ▁目视 -13.5215 ▁翻譯 -13.5216 岫 -13.5217 ▁第五套 -13.5218 ▁夺命 -13.522 ▁闹市 -13.522 建工 -13.522 978 -13.5221 执法 -13.5222 ▁瑶瑶 -13.5223 ▁民情 -13.5223 助理 -13.5224 桉 -13.5227 硬盘 -13.5228 ▁爬虫 -13.5228 ▁德鲁 -13.5229 富贵 -13.5229 势所 -13.523 ▁觀光 -13.523 耶稣 -13.523 ▁卡巴斯基 -13.5231 ▁負擔 -13.5232 ▁卡耐 -13.5234 ries -13.5234 ▁仰面 -13.5234 楦 -13.5236 ▁力帆 -13.5237 Pad -13.5237 侮辱 -13.5238 恍惚 -13.524 ▁拔尖 -13.5242 ▁灰飞烟 -13.5243 117 -13.5244 ▁迷彩 -13.5244 ▁阿姆斯特 -13.5244 闠 -13.5244 ▁幽兰 -13.5244 ▁圆筒 -13.5244 ▁东道主 -13.5248 ▁问鼎 -13.5248 ▁农商 -13.5249 ▁一百六十 -13.525 park -13.525 煩 -13.5251 ▁收費 -13.5252 ▁枇杷 -13.5253 ▁树根 -13.5255 ▁定瓶 -13.5256 ▁阻滞 -13.5257 ▁互鉴 -13.5257 转印 -13.5257 ▁锅底 -13.526 醤 -13.5261 ▁狂飙 -13.5262 ▁趁热 -13.5263 ▁斩首 -13.5263 ▁绵羊 -13.5264 ▁撑开 -13.5264 ▁辍学 -13.5264 ▁陪你 -13.5264 order -13.5265 ▁蜂巢 -13.5265 ▁鹤壁 -13.5265 ▁晚些 -13.5266 878 -13.5267 美食 -13.5267 骋 -13.5268 ▁1927 -13.5269 677 -13.5269 ▁热乎 -13.5269 ▁脉动 -13.527 ▁試驗 -13.527 ▁酣畅 -13.5271 ▁瑞丽 -13.5273 4.6 -13.5273 推行 -13.5275 ▁檀香 -13.5276 言秽 -13.5278 ▁罪孽 -13.5279 ▁丁浩 -13.528 ▁瑞克 -13.5281 ▁FBI -13.5284 工夫 -13.5284 美奂 -13.5285 421 -13.5285 ▁限售 -13.5285 ▁DV -13.5286 讫 -13.5287 ▁速成 -13.5288 公公 -13.5289 ▁Paul -13.529 习气 -13.529 ower -13.529 579 -13.5292 ▁統一 -13.5292 体表 -13.5292 ▁浮料 -13.5295 妙妙 -13.5296 汉语 -13.5296 ▁崇文 -13.5299 ▁独木 -13.5299 ▁发际 -13.53 壒 -13.5302 ▁启闭 -13.5302 遗憾 -13.5302 ▁招工 -13.5302 ▁鬼谷 -13.5303 ▁下厨 -13.5304 ▁离散 -13.5305 ▁常备 -13.5305 动摇 -13.5306 151 -13.5306 相对 -13.5307 ▁bwin -13.5307 冶金 -13.5308 ▁荣登 -13.5308 芥 -13.5308 ▁广进 -13.5308 ▁貂蝉 -13.5308 ▁治水 -13.5311 ┻ -13.5311 ▁10.2 -13.5311 持續 -13.5311 ▁租售 -13.5312 攝 -13.5313 ▁贾诩 -13.5314 ▁家私 -13.5315 ▁送医 -13.5315 信赖 -13.5315 ▁正欲 -13.5317 ▁遇事 -13.5317 ▁一左一 -13.5318 ▁福寿 -13.5318 ▁光驱 -13.5319 ▁盐湖 -13.5319 彌 -13.5319 ▁柱塞 -13.532 ▁大惊小 -13.532 芳芳 -13.532 ▁足球赛 -13.5321 ▁柳絮 -13.5321 牽 -13.5322 609 -13.5323 ▁two -13.5323 ▁南无 -13.5325 ▁泄密 -13.5325 ▁桃红 -13.5326 乜 -13.5327 ▁干净利 -13.5328 ▁烟灰 -13.5329 ▁老丈 -13.5329 257 -13.5329 譙 -13.533 ▁推论 -13.533 欢庆 -13.5331 ▁鼻息 -13.5331 媠 -13.5332 ▁森林狼 -13.5333 race -13.5333 ▁昙花 -13.5334 ▁继母 -13.5335 一番 -13.5336 ▁体弱 -13.5336 痂 -13.5337 ▁邓超 -13.5337 ▁王珉 -13.5338 ▁力挺 -13.5338 ▁脑残 -13.5339 ▁專門 -13.534 ▁一邊 -13.5341 ▁規則 -13.5341 胫 -13.5342 ▁官能 -13.5344 ▁碳素 -13.5345 ▁丫丫 -13.5348 ▁数千万 -13.5348 骐 -13.5349 ▁每时每刻 -13.535 ▁所罗门 -13.535 宽恕 -13.5352 ▁九十九 -13.5355 ▁梳洗 -13.5355 ism -13.5355 ▁佛罗伦萨 -13.5355 ▁入夜 -13.5356 ▁轨制 -13.5356 ▁维基百 -13.5357 ▁黑黑 -13.5358 ▁西伯利亚 -13.5358 ▁Free -13.5358 ▁河马 -13.5359 撷 -13.5359 ▁张掖 -13.5361 east -13.5362 ▁亲卫 -13.5364 ates -13.5364 蟠 -13.5365 ▁认认 -13.5365 凑 -13.5365 ▁相對 -13.5365 ▁乙级 -13.5366 ▁装傻 -13.5366 岁月 -13.5366 緩 -13.5368 见识 -13.5369 ▁小猴 -13.537 2.9 -13.5371 珞 -13.5372 ▁分類 -13.5373 ▁安邦 -13.5374 ▁悻悻 -13.5376 參 -13.5377 ▁肺结核 -13.5377 ▁蒙古人 -13.5377 ▁兴安 -13.5377 ▁创维 -13.5378 ▁几滴 -13.538 519 -13.538 ▁差旅 -13.5381 ▁HDMI -13.5381 作画 -13.5381 ▁紧追 -13.5382 ▁HPV -13.5382 祢 -13.5384 ▁阿凡 -13.5384 ▁喜迎 -13.5384 ▁栀子 -13.5384 ▁韵律 -13.5385 512 -13.5385 ▁面不改 -13.5385 ▁森严 -13.5386 ▁烘托 -13.5386 侯爷 -13.5386 631 -13.5386 ▁查核 -13.5387 雏 -13.5387 ▁鸡尾酒 -13.5389 ▁滤料 -13.539 ▁串口 -13.5391 ▁雪上加 -13.5392 ▁艾玛 -13.5394 lc -13.5394 ▁逐级 -13.5395 屁股 -13.5397 816 -13.5397 宾馆 -13.5398 无惧 -13.5398 ▁肖华 -13.5399 ▁士林 -13.5399 ▁村支 -13.5399 788 -13.54 ▁周维清 -13.54 ▁Ke -13.54 ▁转嫁 -13.54 知识产权 -13.5401 暑假 -13.5404 ▁电荷 -13.5404 ▁艾斯 -13.5404 ▁永定 -13.5404 ▁暴虐 -13.5405 ▁血魔 -13.5407 ▁苏苏 -13.5409 ▁举目 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-13.9931 拏 -13.9933 氓 -13.9933 慘 -13.9938 鳃 -13.9938 菖 -13.9961 垩 -14 檯 -14 耦 -14.0032 膨 -14.0037 筜 -14.0038 砺 -14.0044 蟢 -14.0047 鸠 -14.0053 鍾 -14.006 锻 -14.0064 喅 -14.0064 ゥ -14.0066 苟 -14.0069 廈 -14.0073 氖 -14.0073 讹 -14.0091 摝 -14.0103 穢 -14.0103 厤 -14.0114 镕 -14.0114 怠 -14.0119 襪 -14.0129 寜 -14.0136 窇 -14.014 婀 -14.0142 剎 -14.0147 扒 -14.0157 噫 -14.0162 聽 -14.0168 懼 -14.0173 涣 -14.0175 啋 -14.0175 闱 -14.0184 蚠 -14.0188 稗 -14.0203 溏 -14.0205 儓 -14.0225 曘 -14.0225 飄 -14.0227 荧 -14.0231 犮 -14.0232 偗 -14.0243 苸 -14.0254 馗 -14.026 羔 -14.0284 琥 -14.0285 钂 -14.0287 懠 -14.0293 罵 -14.03 绛 -14.0307 啮 -14.0309 摱 -14.0318 幹 -14.0318 眳 -14.032 蓑 -14.0324 孳 -14.0335 γ -14.0342 蹊 -14.036 玳 -14.036 珪 -14.0364 颔 -14.0367 婼 -14.0369 睶 -14.0375 湳 -14.0378 崱 -14.0382 儆 -14.0391 檻 -14.0398 綱 -14.0398 檸 -14.0404 ┛ -14.0418 珅 -14.042 檄 -14.0425 鑿 -14.0431 楸 -14.0436 蚜 -14.0456 畲 -14.0458 睙 -14.0463 僭 -14.0465 ┒ -14.0476 湃 -14.0478 悥 -14.0487 ず -14.0492 嵎 -14.0492 崉 -14.0496 巩 -14.0498 碁 -14.0499 坥 -14.051 噹 -14.0515 簽 -14.0519 忖 -14.0524 蚥 -14.0539 羍 -14.0542 墽 -14.0544 膻 -14.0551 畀 -14.0553 癜 -14.0555 俠 -14.0562 渿 -14.0564 棯 -14.0571 傍 -14.0572 噶 -14.0579 铄 -14.0583 遼 -14.0589 憳 -14.0592 蕲 -14.0596 厠 -14.0601 虁 -14.0601 鸩 -14.0601 纨 -14.0603 韕 -14.0603 鸷 -14.0603 惕 -14.0605 嗪 -14.0605 銈 -14.0605 蝗 -14.0609 葡 -14.0611 髦 -14.063 姤 -14.064 鉛 -14.064 螭 -14.0649 岘 -14.0653 懶 -14.0656 頃 -14.0669 羧 -14.0674 燧 -14.0676 莪 -14.0695 暂 -14.0696 繇 -14.0697 跶 -14.0697 龜 -14.0699 褝 -14.0704 赅 -14.0706 嘢 -14.0708 擇 -14.071 о -14.0711 颢 -14.0736 郃 -14.0738 徹 -14.0754 臘 -14.0768 镭 -14.0771 Е -14.0771 誊 -14.0775 壆 -14.0778 酆 -14.078 犸 -14.0787 嚐 -14.0794 柩 -14.0803 砭 -14.0803 蝴 -14.0812 騼 -14.0812 慣 -14.0815 杵 -14.0815 灬 -14.0829 邨 -14.0829 颁 -14.0832 篪 -14.0833 偧 -14.0838 痚 -14.0843 執 -14.0849 祜 -14.085 蘊 -14.0854 屍 -14.0857 谚 -14.0861 诳 -14.0868 褚 -14.0882 ア -14.0885 綜 -14.0894 稞 -14.0897 蚑 -14.0901 洺 -14.0904 邙 -14.0908 厱 -14.0913 葫 -14.092 讧 -14.0925 伓 -14.0927 皎 -14.0927 既 -14.0941 頬 -14.0941 昉 -14.0948 碚 -14.0951 樊 -14.0954 櫎 -14.0955 苾 -14.096 窸 -14.0984 蜘 -14.0986 刽 -14.0991 剳 -14.0991 瓤 -14.0991 槛 -14.0991 庛 -14.0996 塩 -14.1005 蹦 -14.1008 寳 -14.1024 ▉ -14.1026 ト -14.1026 販 -14.1038 棂 -14.1043 斡 -14.1048 蚱 -14.1053 ┿ -14.1055 黜 -14.1062 ゼ -14.1067 惛 -14.1072 澍 -14.1079 骈 -14.1091 釘 -14.1098 薅 -14.1108 遜 -14.1108 亹 -14.111 儹 -14.1112 钏 -14.1115 驷 -14.1117 蛐 -14.1136 嫆 -14.1141 腭 -14.1144 諷 -14.1144 誼 -14.1158 紐 -14.116 奧 -14.1162 摔 -14.1163 聒 -14.1163 ˋ -14.1165 嬌 -14.1165 谡 -14.1168 蜇 -14.117 姙 -14.1172 鴨 -14.1175 ∧ -14.1189 鈴 -14.1199 妧 -14.1201 純 -14.1202 巖 -14.1209 骶 -14.1216 呋 -14.1221 彟 -14.1221 炙 -14.1224 枬 -14.1226 緬 -14.1226 恫 -14.1228 魍 -14.1228 倶 -14.1231 暎 -14.125 醺 -14.1252 仝 -14.1252 韹 -14.1262 ∕ -14.1272 囿 -14.1274 艷 -14.1279 冯 -14.1286 喹 -14.1294 晠 -14.1301 镔 -14.1321 р -14.133 琍 -14.133 渚 -14.1331 蓐 -14.134 ∏ -14.1343 谯 -14.1343 砙 -14.1345 湟 -14.1362 颛 -14.1365 唁 -14.1367 棦 -14.1372 舛 -14.1372 釀 -14.1377 哗 -14.1379 鸔 -14.1382 硩 -14.1385 舸 -14.1392 幺 -14.1396 诮 -14.1397 軴 -14.1397 侑 -14.1409 殣 -14.1409 輯 -14.1416 賊 -14.1429 潍 -14.1434 誦 -14.1436 靼 -14.1454 錧 -14.1456 賜 -14.1461 尯 -14.1481 痨 -14.1481 卮 -14.1484 リ -14.1494 呤 -14.1494 嚩 -14.1499 褟 -14.1501 璭 -14.1508 瘉 -14.1521 杲 -14.1523 墮 -14.1526 苣 -14.1533 鐍 -14.1533 辗 -14.1536 蚩 -14.1536 堅 -14.1537 贮 -14.1539 艌 -14.1546 芫 -14.1546 砒 -14.1553 窩 -14.1561 魃 -14.1566 憶 -14.1568 賠 -14.1569 濠 -14.1572 ゃ -14.1584 é -14.1586 懑 -14.1589 歙 -14.1589 愍 -14.1591 纥 -14.1599 熄 -14.1604 拺 -14.1614 喱 -14.1624 帿 -14.1624 忏 -14.1636 滂 -14.1642 簸 -14.1644 阇 -14.1647 ˇ -14.1657 卂 -14.1667 鸪 -14.1669 躺 -14.1673 ╔ -14.168 埚 -14.169 僮 -14.1695 耒 -14.1697 鍡 -14.17 浏 -14.1713 鉔 -14.1713 寵 -14.173 饴 -14.1743 藍 -14.1754 崕 -14.1758 徊 -14.1758 鲖 -14.1758 駁 -14.1764 旖 -14.1769 郅 -14.1776 囵 -14.1782 堀 -14.1782 毘 -14.1799 谘 -14.1802 霰 -14.1805 崏 -14.1815 縍 -14.1817 奄 -14.182 疥 -14.1823 垙 -14.1825 苡 -14.1828 疡 -14.1833 蚹 -14.1833 ̇ -14.1835 炁 -14.1835 锑 -14.1835 旸 -14.1838 晳 -14.1841 緷 -14.1843 脐 -14.1846 叴 -14.1848 婠 -14.1861 魉 -14.1874 灑 -14.1892 墉 -14.1895 縹 -14.1895 汜 -14.1903 俎 -14.1908 衅 -14.1912 彴 -14.1913 寔 -14.1916 甆 -14.1921 竊 -14.1924 簌 -14.1924 ∮ -14.1926 锗 -14.1929 溿 -14.1931 毊 -14.1939 エ -14.1947 妊 -14.1947 廁 -14.195 洹 -14.1952 葚 -14.1955 騷 -14.196 埮 -14.1963 萒 -14.1989 蘿 -14.1991 イ -14.1999 鵬 -14.2002 涮 -14.2002 ˉ -14.201 黏 -14.2011 ╰ -14.2015 迄 -14.2015 宑 -14.2023 粲 -14.2025 釣 -14.2025 鬩 -14.2031 亓 -14.2033 绚 -14.2044 姽 -14.2044 阖 -14.2045 咛 -14.2056 惫 -14.206 ̈ -14.2062 ㄢ -14.2062 砲 -14.2062 鞠 -14.2069 躲 -14.2071 匝 -14.2072 ハ -14.2081 叮 -14.2085 ニ -14.2086 禱 -14.2091 蛀 -14.2096 遑 -14.2097 鑜 -14.2102 崟 -14.2107 暝 -14.2107 撮 -14.2115 砥 -14.2118 蔺 -14.2123 镊 -14.2131 瞅 -14.2137 螟 -14.2142 呂 -14.2144 捺 -14.2153 埕 -14.2168 衙 -14.2174 咭 -14.2182 笆 -14.2182 胚 -14.2183 跎 -14.2187 纠 -14.2188 儳 -14.2195 爞 -14.2195 綑 -14.2206 媾 -14.2208 礽 -14.2208 暈 -14.2211 拭 -14.2212 胱 -14.2214 蹙 -14.2215 禩 -14.2246 禍 -14.2254 醐 -14.2254 ╜ -14.2257 鲢 -14.2262 傣 -14.2269 垵 -14.2273 萋 -14.2278 潸 -14.2281 韓 -14.2284 潑 -14.2286 ∈ -14.2292 嫍 -14.2292 ö -14.2294 僖 -14.2297 顛 -14.2302 勸 -14.2308 牡 -14.2312 嘈 -14.2313 趺 -14.2316 刎 -14.2319 鎳 -14.2321 韣 -14.2321 锰 -14.2322 榉 -14.2329 叻 -14.2338 甙 -14.2338 濃 -14.2347 熔 -14.2348 欤 -14.2351 葺 -14.2351 苻 -14.2359 睐 -14.2361 馳 -14.2365 溾 -14.237 锾 -14.2373 и -14.2384 佶 -14.2384 旳 -14.2386 悄 -14.2391 蒋 -14.2394 襕 -14.2395 每 -14.241 揆 -14.2414 帺 -14.2419 砆 -14.2419 汾 -14.2427 巹 -14.2427 蒼 -14.2427 赡 -14.243 旎 -14.2433 嘯 -14.2436 喋 -14.2438 裾 -14.2438 偛 -14.2446 蹟 -14.2446 陼 -14.2446 蠟 -14.246 揵 -14.2466 娩 -14.247 镐 -14.2472 蔼 -14.2477 搬 -14.2477 浐 -14.2479 乞 -14.2484 挹 -14.2485 頌 -14.2488 氅 -14.249 呵 -14.2497 饋 -14.2499 籠 -14.2504 椰 -14.2512 帚 -14.2515 馔 -14.2518 侈 -14.2521 嘻 -14.2527 犟 -14.2529 馒 -14.2543 漊 -14.2543 ╯ -14.2546 诌 -14.2548 嫑 -14.2551 贽 -14.2554 濫 -14.2562 豈 -14.2584 瓨 -14.2587 桠 -14.2601 殓 -14.2604 荪 -14.2604 疳 -14.2612 羯 -14.2613 攫 -14.2615 菸 -14.2618 釼 -14.2621 枒 -14.2635 潬 -14.2637 畼 -14.2651 碣 -14.2654 橏 -14.2657 逍 -14.2661 憬 -14.2662 彧 -14.2664 鐡 -14.2665 帹 -14.2674 氐 -14.2674 偵 -14.2682 雌 -14.2687 懲 -14.2688 睜 -14.2688 嗥 -14.2691 髰 -14.2691 糾 -14.2693 拈 -14.2694 吆 -14.2696 僑 -14.2696 卟 -14.2696 敮 -14.2699 梾 -14.2699 鄧 -14.2702 鬱 -14.2716 闩 -14.2719 亜 -14.2733 弰 -14.2736 炪 -14.2738 喌 -14.2744 掴 -14.2744 鈣 -14.2747 矜 -14.2753 谄 -14.2758 惞 -14.2764 蔛 -14.2767 叢 -14.2786 仞 -14.2788 姢 -14.2789 憉 -14.2792 ║ -14.2795 瘔 -14.2806 椈 -14.2812 繊 -14.2812 铖 -14.2815 涸 -14.2847 燚 -14.2855 苁 -14.2858 吭 -14.2858 泅 -14.286 た -14.2872 猗 -14.2875 裘 -14.2875 ∞ -14.288 圜 -14.2886 桵 -14.2892 嫃 -14.2895 孰 -14.2895 惁 -14.2903 掼 -14.2909 麂 -14.2912 燦 -14.2918 ∫ -14.2923 槁 -14.2932 铣 -14.2933 莖 -14.2935 ク -14.2955 蘇 -14.2962 喧 -14.2971 尋 -14.2977 睷 -14.2987 捱 -14.299 嗏 -14.2993 俚 -14.3001 肓 -14.3019 巻 -14.3022 蕤 -14.3022 し -14.3024 茴 -14.3029 岿 -14.3033 ∥ -14.3036 斂 -14.3039 蔣 -14.3042 鉆 -14.3048 觥 -14.3054 埡 -14.3056 轟 -14.3059 籲 -14.3062 尻 -14.3068 オ -14.3071 陜 -14.308 輻 -14.3083 篁 -14.3086 陰 -14.309 澘 -14.3091 軒 -14.31 惚 -14.3103 崲 -14.3103 瓕 -14.3115 阗 -14.3115 畹 -14.3121 哦 -14.3123 貉 -14.3124 岬 -14.3132 嬰 -14.3135 圻 -14.3135 踒 -14.3138 縺 -14.3141 嫭 -14.3147 佥 -14.3159 砣 -14.3162 撐 -14.3167 溉 -14.3173 ッ -14.3182 崃 -14.3191 薨 -14.3197 臬 -14.3209 鸯 -14.3215 绉 -14.3227 蘍 -14.3233 カ -14.3236 篙 -14.3247 芾 -14.325 値 -14.3253 咤 -14.3255 笄 -14.3271 闽 -14.3279 瑰 -14.3282 崌 -14.3283 菟 -14.3286 媯 -14.3292 虢 -14.3292 嗜 -14.3297 噦 -14.3307 斫 -14.3325 畢 -14.3328 狒 -14.3328 絨 -14.3328 诡 -14.3333 堃 -14.3349 鹹 -14.3349 丼 -14.3355 铬 -14.3358 珥 -14.3376 繋 -14.3376 緥 -14.3379 熵 -14.3385 淸 -14.3388 隸 -14.3391 緝 -14.3394 諎 -14.3397 桡 -14.3403 鍨 -14.3409 ぜ -14.3412 玠 -14.3418 祿 -14.3421 ǒ -14.343 滯 -14.3433 膑 -14.3433 崧 -14.3445 茓 -14.3445 澗 -14.3454 ナ -14.346 徆 -14.3463 咋 -14.3475 蜻 -14.3479 儫 -14.3482 伢 -14.3485 賬 -14.3488 粸 -14.3488 珒 -14.35 蔥 -14.3503 ロ -14.3506 纏 -14.3506 蚵 -14.3506 枂 -14.3521 説 -14.3524 啉 -14.3527 坷 -14.353 蚤 -14.3536 ❀ -14.3552 廂 -14.3552 赟 -14.3552 鰯 -14.3555 瞾 -14.3558 弁 -14.3561 邰 -14.3561 嶼 -14.3579 芨 -14.3579 殁 -14.3586 戊 -14.359 亶 -14.3592 彥 -14.3604 蒡 -14.3604 雎 -14.3619 驚 -14.3622 鲍 -14.3622 誯 -14.3622 眙 -14.3629 叱 -14.3634 齆 -14.3644 茏 -14.3647 獾 -14.3653 サ -14.3656 掬 -14.3671 鷹 -14.3672 珷 -14.3675 喑 -14.3678 餓 -14.3678 棝 -14.3681 韋 -14.3681 湧 -14.369 兖 -14.3696 蜗 -14.3709 兌 -14.3709 秣 -14.3709 馮 -14.3718 簻 -14.3734 繍 -14.3734 紩 -14.3746 倆 -14.3753 荑 -14.3753 凄 -14.377 猥 -14.3771 皺 -14.3771 盃 -14.3771 寑 -14.3781 淦 -14.3781 舂 -14.3781 櫓 -14.3784 ˊ -14.379 ゚ -14.38 枞 -14.3806 魈 -14.3806 粛 -14.3822 茭 -14.3828 佗 -14.383 遐 -14.3834 暱 -14.3837 鍥 -14.3837 棗 -14.384 刕 -14.3844 鍿 -14.3844 孀 -14.385 凇 -14.3869 塬 -14.3869 镍 -14.3873 箟 -14.3875 瓮 -14.3882 跖 -14.3885 鎿 -14.3888 鄏 -14.39 憤 -14.3907 叿 -14.3913 酊 -14.392 蹴 -14.3929 レ -14.3932 臑 -14.3939 鵝 -14.3939 寷 -14.3951 彞 -14.3951 ╟ -14.3955 湊 -14.3955 崛 -14.3963 窠 -14.3964 笟 -14.3967 櫥 -14.397 炕 -14.3976 蘆 -14.3983 鋅 -14.3983 箰 -14.3999 蹭 -14.4002  -14.4002 礊 -14.4006 萻 -14.4031 衄 -14.4038 猊 -14.4044 毚 -14.4054 瑀 -14.4054 碜 -14.4063 蠹 -14.4063 銜 -14.4067 鬡 -14.4067 剝 -14.4076 绺 -14.4076 ˹ -14.4083 缢 -14.4083 緒 -14.4088 + -14.4106 愘 -14.4109 鶴 -14.4111 叺 -14.4115 霹 -14.4115 這 -14.4117 仺 -14.4118 镛 -14.4122 烩 -14.4127 迂 -14.4128 旃 -14.4131 蓼 -14.4131 诅 -14.4141 墐 -14.4144 鳶 -14.4151 ┐ -14.416 痼 -14.416 懗 -14.4164 棷 -14.4167 缰 -14.4173 笳 -14.4183 箜 -14.4183 吒 -14.4191 誇 -14.42 龔 -14.4206 粐 -14.4213 浒 -14.4218 彭 -14.4223 谔 -14.4226 鲫 -14.4229 懇 -14.4229 癒 -14.4229 啾 -14.423 箸 -14.4232 雓 -14.4236 鮛 -14.4242 泯 -14.4253 ě -14.4258 殦 -14.4262 綈 -14.4262 钿 -14.4272 鲷 -14.4275 甾 -14.4288 臢 -14.4295 檮 -14.4314 赁 -14.4331 嚯 -14.4331 掋 -14.4334 フ -14.4341 琵 -14.4341 笇 -14.4344 缮 -14.4347 蝈 -14.4354 匮 -14.4354 禺 -14.4357 畿 -14.436 囔 -14.4372 垃 -14.4374 悌 -14.4374 焖 -14.4374 な -14.4377 悱 -14.4377 乭 -14.4381 犍 -14.4381 ╮ -14.4387 諦 -14.4387 讥 -14.4408 唛 -14.4414 祕 -14.4414 挣 -14.4415 貞 -14.4421 媱 -14.4427 н -14.4437 紆 -14.4451 逞 -14.4453 耋 -14.4454 菓 -14.4457 £ -14.4464 悼 -14.4472 締 -14.4484 俅 -14.4491 蹑 -14.4494 肛 -14.4497 痪 -14.4504 ァ -14.4504 噼 -14.4511 嵬 -14.4511 啒 -14.4525 闖 -14.4525 遒 -14.4528 陟 -14.4531 儨 -14.4538 粎 -14.4538 ケ -14.4548 峒 -14.4548 テ -14.4552 泱 -14.4558 忒 -14.4569 崭 -14.4596 暹 -14.463 橄 -14.4637 搐 -14.467 屉 -14.4673 邓 -14.4694 阮 -14.4726 採 -14.4738 蜓 -14.476 谩 -14.4816 窿 -14.4924 豺 -14.4945 殡 -14.4946 婪 -14.4983 仍 -14.5014 玻 -14.5046 鱿 -14.5169 熨 -14.5196 掃 -14.5222 遵 -14.5236 郜 -14.5281 嫖 -14.5292 肪 -14.5303 劾 -14.5324 螨 -14.5366 拯 -14.5415 預 -14.5438 鳄 -14.5502 惰 -14.5547 廢 -14.5589 踐 -14.5599 瘠 -14.5625 衮 -14.5635 堕 -14.5635 钨 -14.5658 紹 -14.5666 芎 -14.567 铂 -14.5674 龈 -14.5678 茁 -14.5712 矸 -14.5777 蠕 -14.5786 荀 -14.588 纜 -14.588 忤 -14.5892 唬 -14.6026 浜 -14.6047 噩 -14.606 黔 -14.6066 嫦 -14.6123 榈 -14.6131 咖 -14.6139 髅 -14.6151 镶 -14.6212 奂 -14.6233 戬 -14.6237 莅 -14.627 毋 -14.6286 圾 -14.6286 敝 -14.6302 罂 -14.6319 徇 -14.6367 沒 -14.6384 纫 -14.6428 柑 -14.6434 咄 -14.6461 骼 -14.6489 楹 -14.6514 嚅 -14.6522 佼 -14.6535 刪 -14.6539 甬 -14.6543 荤 -14.6573 藓 -14.658 甥 -14.659 聂 -14.661 狈 -14.6613 郸 -14.6718 阎 -14.6777 寬 -14.6803 珮 -14.6816 斟 -14.6816 饲 -14.6839 瘪 -14.6844 掰 -14.6846 惋 -14.69 锺 -14.6946 蟆 -14.6947 椹 -14.6951 骜 -14.6964 秸 -14.7003 貢 -14.706 惦 -14.7099 菏 -14.7169 稔 -14.7173 蛔 -14.72 榛 -14.7262 颓 -14.7267 扪 -14.7279 沭 -14.7288 腋 -14.7357 橇 -14.7395 噴 -14.7398 閭 -14.7403 俞 -14.7463 咧 -14.7466 棣 -14.7559 鹦 -14.7568 妪 -14.7577 蹿 -14.7658 懵 -14.7663 嬴 -14.7682 赂 -14.7724 哺 -14.7742 枸 -14.7743 拷 -14.7743 訾 -14.7818 嗫 -14.7869 涓 -14.789 薦 -14.7933 覃 -14.7935 憧 -14.7973 詹 -14.7978 儡 -14.8035 娄 -14.8189 稣 -14.8312 烘 -14.8314 刨 -14.8318 嘀 -14.8319 蜈 -14.8377 炅 -14.841 橡 -14.8418 蜕 -14.8445 鹑 -14.8466 谆 -14.8511 诧 -14.8526 诽 -14.8541 纾 -14.8677 另 -14.8698 漉 -14.8709 卤 -14.876 餮 -14.8766 狰 -14.8833 亳 -14.891 阱 -14.8932 阆 -14.8958 殘 -14.901 甄 -14.9069 硼 -14.9085 鍍 -14.9091 毂 -14.9165 潛 -14.9195 挎 -14.9277 叵 -14.9305 耽 -14.9405 荥 -14.9481 鑰 -14.9529 汲 -14.9586 螳 -14.9649 阐 -14.9729 濡 -14.9776 鸵 -14.9796 奚 -14.981 奘 -14.9819 牺 -14.9882 脅 -14.9899 谲 -14.9928 搂 -14.9993 芭 -15.0017 匍 -15.0033 樺 -15.0053 競 -15.0077 挝 -15.0117 诿 -15.0119 葩 -15.0233 咨 -15.0276 櫧 -15.0343 夙 -15.0362 獠 -15.0368 泞 -15.0378 倜 -15.0427 粼 -15.0494 翌 -15.0511 阈 -15.0537 氫 -15.0561 擞 -15.0597 烙 -15.0599 殉 -15.0618 冗 -15.0618 偻 -15.0636 啕 -15.0698 氦 -15.0726 隨 -15.0732 訶 -15.0818 叩 -15.0834 螂 -15.0843 礎 -15.0874 迢 -15.0964 娉 -15.1011 廖 -15.1077 捡 -15.1103 鑾 -15.1217 鼬 -15.123 踉 -15.1251 遨 -15.1304 眨 -15.1392 翟 -15.1399 镝 -15.1413 狞 -15.1516 搡 -15.1532 缭 -15.1583 骆 -15.1599 跄 -15.1683 刁 -15.1705 嵋 -15.1787 倭 -15.1825 ō -15.1839 跺 -15.1839 喽 -15.1882 催 -15.1889 靳 -15.1926 纰 -15.1962 烏 -15.2188 碘 -15.2233 聆 -15.23 蹶 -15.2324 嗡 -15.2345 臾 -15.2353 鄱 -15.2376 傥 -15.239 佃 -15.2428 幄 -15.251 蛰 -15.2626 憑 -15.2627 矫 -15.2657 咂 -15.2673 咙 -15.2798 疙 -15.2798 淆 -15.286 莠 -15.2891 窕 -15.2962 呜 -15.2964 颯 -15.3004 龌 -15.3025 躇 -15.3064 蘋 -15.3072 祛 -15.3132 伉 -15.3152 咫 -15.32 苜 -15.3264 臀 -15.3306 謗 -15.3321 骰 -15.3481 飨 -15.3489 铈 -15.3599 薛 -15.3616 倨 -15.3702 啧 -15.3762 嬉 -15.3762 脍 -15.3778 昵 -15.3788 尴 -15.3795 帼 -15.3872 蚓 -15.3881 邏 -15.3941 饪 -15.4045 镳 -15.4062 菠 -15.4079 擅 -15.4088 磋 -15.4168 啬 -15.4169 詮 -15.4248 á -15.4296 锲 -15.4302 錘 -15.4332 瘩 -15.4338 锛 -15.4341 劉 -15.4387 崔 -15.4406 擺 -15.4424 娠 -15.4437 褛 -15.4437 娛 -15.4483 撩 -15.4536 豌 -15.4538 抛 -15.4554 蓿 -15.4667 搪 -15.4744 訟 -15.4789 猬 -15.4807 丢 -15.482 騎 -15.484 袁 -15.4898 潺 -15.4931 澧 -15.4956 裴 -15.4975 绔 -15.5015 蓟 -15.5024 誹 -15.5037 栾 -15.5084 撕 -15.5183 扳 -15.5203 奠 -15.5233 昙 -15.5233 蝙 -15.5251 狡 -15.5253 雳 -15.534 烹 -15.5415 猝 -15.5466 搔 -15.5497 褒 -15.5506 胰 -15.5507 賃 -15.5531 癸 -15.5621 捋 -15.5652 卻 -15.5715 赳 -15.5726 吳 -15.5736 缅 -15.5779 糯 -15.5789 怦 -15.5829 娓 -15.5939 疸 -15.606 佔 -15.608 龊 -15.6115 唔 -15.6256 俯 -15.6289 瀏 -15.63 扼 -15.6344 滇 -15.6424 啥 -15.6459 遺 -15.6595 镓 -15.6722 驸 -15.6748 汴 -15.6757 恍 -15.6862 溧 -15.6874 í -15.6898 抿 -15.6945 皈 -15.6957 滄 -15.6993 痉 -15.7223 绷 -15.7445 拴 -15.7445 邹 -15.7559 儋 -15.7669 绯 -15.7802 汩 -15.7822 唏 -15.7828 廿 -15.7841 蚣 -15.8028 鹉 -15.8028 悻 -15.8186 淬 -15.8226 嘌 -15.8305 冪 -15.8321 仃 -15.8481 哆 -15.8514 曠 -15.8612 浠 -15.8825 胛 -15.8827 妫 -15.8926 磕 -15.8984 浃 -15.907 è -15.9116 袤 -15.9142 掏 -15.916 鏖 -15.9171 枷 -15.9205 涔 -15.9317 虬 -15.9325 捻 -15.937 凋 -15.9523 偃 -15.957 隍 -15.9587 楊 -15.9741 呗 -15.9789 逅 -15.9803 毗 -15.9804 郝 -15.982 惴 -15.985 怎 -15.9946 ! -15.9964 锄 -16.0045 骡 -16.0126 靛 -16.0391 驮 -16.0408 戛 -16.0459 鑽 -16.0459 擴 -16.051 谴 -16.0595 啃 -16.0629 饨 -16.0799 忑 -16.0886 蘸 -16.0944 澶 -16.0961 鑲 -16.1068 嗎 -16.1342 嗷 -16.1379 抠 -16.1379 娑 -16.1435 岌 -16.1511 姒 -16.1642 吩 -16.1661 渤 -16.1969 坜 -16.2037 杷 -16.2377 匕 -16.2454 ā -16.2579 獗 -16.2642 趄 -16.2704 她 -16.2705 娈 -16.2769 瞌 -16.2787 搶 -16.279 榫 -16.2811 匐 -16.3042 撲 -16.3204 坍 -16.3249 褴 -16.3259 旮 -16.3347 朕 -16.3659 黝 -16.3706 哪 -16.3891 碾 -16.3895 溴 -16.3919 趁 -16.3966 陡 -16.4039 迳 -16.4308 瞄 -16.4333 啄 -16.4508 蜴 -16.4697 鄭 -16.495 憔 -16.495 掐 -16.5003 趔 -16.5164 , -16.53 篝 -16.5405 鍛 -16.555 痍 -16.5653 徜 -16.5653 鐮 -16.5749 俨 -16.5749 旯 -16.5879 邢 -16.5952 徉 -16.5965 岖 -16.6081 賺 -16.6159 / -16.6219 迥 -16.6649 掳 -16.6775 拧 -16.6839 爰 -16.6936 豁 -16.7033 啊 -16.7065 诬 -16.7636 矗 -16.7638 閬 -16.7638 臧 -16.7708 縊 -16.7778 撻 -16.8099 浣 -16.8172 沮 -16.8209 瞿 -16.8432 閾 -16.8583 腌 -16.8775 “ -16.8814 诠 -16.8814 曝 -16.8851 剃 -16.8853 篑 -16.9092 雖 -16.9293 嗤 -16.9839 濮 -17.0236 漩 -17.0326 鐫 -17.0649 ú -17.1031 缥 -17.1129 龚 -17.1684 岂 -17.1841 蜊 -17.1924 捶 -17.2383 咩 -17.2609 扛 -17.2839 誰 -17.2898 佟 -17.3135 蹇 -17.3256 。 -17.3691 姹 -17.4014 蛻 -17.4014 婁 -17.4146 敕 -17.4279 姘 -17.5883 唉 -17.5962 撬 -17.6042 昨 -17.6123 à -17.6204 ī -17.6369 囤 -17.6706 嗚 -17.6793 踩 -17.6793 扔 -17.688 幌 -17.7145 椋 -17.7235 篡 -17.7235 α -17.8694 《 -17.8906 撅 -17.9122 贰 -17.9232 、 -17.9569 晾 -17.9684 胯 -18.0157 ? -18.1588 闫 -18.2934 噗 -18.3095 偌 -18.3259 с -18.4121 搴 -18.4303 硒 -18.4303 т -18.4303 拄 -18.4488 у -18.4488 喔 -18.4869 嗨 -18.4869 琚 -18.5065 麾 -18.5265 : -18.5469 嘤 -18.5469 舐 -18.5891 掀 -18.6108 钪 -18.6108 μ -18.6557 妳 -18.679 窒 -18.7028 伫 -18.7522 嚦 -18.7522 揩 -18.7522 瀹 -18.7522 逮 -18.7778 抉 -18.7778 ò -18.8042 【 -18.8042 堝 -18.8042 拚 -18.8312 舀 -18.859 卞 -18.8875 壬 -18.9169 π -18.9785 硷 -18.9785 ( -18.9785 ” -19.0108 δ -19.0786 · -19.0786 唾 -19.0786 隋 -19.1142 [ -19.1143 偈 -19.1143 钕 -19.1898 踊 -19.2298 繃 -19.2298 啜 -19.3604 甩 -19.408 羟 -19.458 镌 -19.458 ч -19.5106 慵 -19.5106 拎 -19.5106 揪 -19.5662 妗 -19.5662 绠 -19.5662 ǎ -19.5662 甯 -19.625 舔 -19.7542 欒 -19.8256 嶄 -19.8256 銆 -19.9025 亟 -19.9859 捅 -19.9859 迸 -19.9859 澹 -20.0768 " -20.0768 俺 -20.0768 氤 -20.0768 β -20.1768 捂 -20.1768 ц -20.1768 「 -20.1768 х -20.2879 】 -20.2879 倏 -20.2879 畬 -20.2879 嫡 -20.4129 憋 -20.4129 掺 -20.4129 タ -20.5558 ; -20.5558 蹲 -20.5558 漕 -20.7224 ス -20.7224 ] -20.9224 》 -20.9224 诋 -20.9224 蛊 -20.9225 诟 -20.9225 ф -20.9225 撂 -21.1725 甭 -21.1725 臃 -21.1725 □ -21.1725 怂 -21.1725 戦 -21.1725 绗 -21.1725 — -21.5059 ★ -21.5059 噢 -21.5059 捎 -21.5059 闇 -21.5059 ～ -21.5059 徳 -21.5059 摁 -22.006 満 -22.006 滀 -22.006 註 -22.006 陛 -22.006 陨 -22.006 * -22.006 > -22.006 ’ -22.006 『 -22.006 剉 -22.006 э -22.006 蒹 -22.9768 讣 -22.9769 谶 -22.977 戕 -22.9771 痱 -22.9772 郓 -22.9773 囫 -22.9774 螓 -22.9775 蓖 -22.9776 渑 -22.9777 铩 -22.9778 囟 -22.9779 傩 -22.978 玔 -22.9781 钁 -22.9782 踰 -22.9783 鹧 -22.9784 抻 -22.9785 悭 -22.9786 盱 -22.9787 龅 -22.9788 柞 -22.9789 颞 -22.979 耄 -22.9791 🌕 -22.9792 齙 -22.9793 赭 -22.9794 瘈 -22.9795 摈 -22.9796 莜 -22.9797 犄 -22.9798 坩 -22.9799 欸 -22.98 蒯 -22.9801 缱 -22.9802 牝 -22.9803 揾 -22.9804 醍 -22.9805 襁 -22.9806 馊 -22.9807 〜 -22.9808 雒 -22.9809 铡 -22.981 辎 -22.9811 儗 -22.9812 蟋 -22.9813 偍 -22.9814 郫 -22.9815 噘 -22.9816 氙 -22.9817 蹉 -22.9818 箬 -22.9819 赝 -22.982 睚 -22.9821 濛 -22.9822 噯 -22.9823 怙 -22.9824 シ -22.9825 嗳 -22.9826 蹩 -22.9827 魑 -22.9828 麋 -22.9829 弸 -22.983 鲶 -22.9831 梖 -22.9832 孱 -22.9833 铤 -22.9834 醴 -22.9835 盥 -22.9836 睥 -22.9837 垝 -22.9838 啫 -22.9839 邝 -22.984 孬 -22.9841 醡 -22.9842 郧 -22.9843 邛 -22.9844 梽 -22.9845 薙 -22.9846 鹌 -22.9847 阚 -22.9848 愠 -22.9849 瘯 -22.985 氩 -22.9851 拋 -22.9852 犒 -22.9853 蝌 -22.9854 鄞 -22.9855 嗬 -22.9856 丟 -22.9857 袈 -22.9858 汨 -22.9859 綦 -22.986 煅 -22.9861 佝 -22.9862 嵊 -22.9863 珐 -22.9864 黉 -22.9865 跛 -22.9866 樋 -22.9867 沏 -22.9868 孪 -22.9869 柊 -22.987 齎 -22.9871 寤 -22.9872 拮 -22.9873 牦 -22.9874 嬁 -22.9875 キ -22.9876 鹈 -22.9877 龇 -22.9878 矇 -22.9879 涿 -22.988 玷 -22.9881 崆 -22.9882 枇 -22.9883 枳 -22.9884 怘 -22.9885 扦 -22.9886 哐 -22.9887 桁 -22.9888 蒐 -22.9889 剽 -22.989 胴 -22.9891 髋 -22.9892 閰 -22.9893 搧 -22.9894 掞 -22.9895 砝 -22.9896 砼 -22.9897 踮 -22.9898 诙 -22.9899 酗 -22.99 崂 -22.9901 喺 -22.9902 蚯 -22.9903 讴 -22.9904 阉 -22.9905 腼 -22.9906 蔔 -22.9907 漯 -22.9908 畠 -22.9909 撵 -22.991 鞑 -22.9911 馄 -22.9912 痤 -22.9913 喎 -22.9914 噱 -22.9915 觊 -22.9916 钣 -22.9917 佘 -22.9918 豢 -22.9919 遛 -22.992 婺 -22.9921 氶 -22.9922 俾 -22.9923 麝 -22.9924 槦 -22.9925 荨 -22.9926 鐤 -22.9927 滁 -22.9928 蚝 -22.9929 侏 -22.993 蝼 -22.9931 橈 -22.9932 螾 -22.9933 瞠 -22.9934 跆 -22.9935 抨 -22.9936 蹂 -22.9937 冮 -22.9938 惆 -22.9939 藐 -22.994 邬 -22.9941 蟑 -22.9942 踌 -22.9943 涳 -22.9944 涢 -22.9945 缜 -22.9946 碉 -22.9947 缂 -22.9948 琊 -22.9949 瘙 -22.995 綍 -22.9951 屌 -22.9952 踱 -22.9953 猕 -22.9954 █ -22.9955 啀 -22.9956 肮 -22.9957 摒 -22.9958 骻 -22.9959 蜿 -22.996 楋 -22.9961 洖 -22.9962 猖 -22.9963 蜷 -22.9964 飓 -22.9965 罹 -22.9966 炰 -22.9967 邂 -22.9968 瞭 -22.9969 裇 -22.997 忚 -22.9971 跻 -22.9972 椭 -22.9973 痊 -22.9974 螃 -22.9975 婄 -22.9976 哽 -22.9977 侀 -22.9978 遴 -22.9979 酝 -22.998 莆 -22.9981 鎭 -22.9982 瞟 -22.9983 搀 -22.9984 忐 -22.9985 胭 -22.9986 彷 -22.9987 婊 -22.9988 惬 -22.9989 粽 -22.999 呦 -22.9991 冨 -22.9992 繬 -22.9993 懊 -22.9994 侥 -22.9995 囨 -22.9996 泸 -22.9997 徘 -22.9998 簨 -22.9999 濇 -23 撳 -23.0001 ョ -23.0002 闃 -23.0003 邯 -23.0004 欑 -23.0005 狙 -23.0006 熺 -23.0007 灞 -23.0008 踹 -23.0009 夌 -23.001 ‧ -23.0011 煽 -23.0012 咔 -23.0013 暧 -23.0014 匈 -23.0015 籗 -23.0016 彲 -23.0017 犱 -23.0018 褪 -23.0019 傝 -23.002 擄 -23.0021 剁 -23.0022 庡 -23.0023 嗅 -23.0024 咆 -23.0025 緱 -23.0026 栧 -23.0027 椂 -23.0028 湴 -23.0029 嬫 -23.003 蓦 -23.0031 氬 -23.0032 骷 -23.0033 鑷 -23.0034 黯 -23.0035 丨 -23.0036 孵 -23.0037 佷 -23.0038 撸 -23.0039 ぇ -23.004 倘 -23.0041 鍘 -23.0042 闂 -23.0043 夊 -23.0044 胳 -23.0045 缁 -23.0046 潵 -23.0047 傚 -23.0048 滃 -23.0049 趴 -23.005 ′ -23.0051 傗 -23.0052 粬 -23.0053 岃 -23.0054 垜 -23.0055 鏉 -23.0056 哎 -23.0057 屼 -23.0058 竴 -23.0059 鏄 -23.006 钬 -23.0061 屽 -23.0062 鈥 -23.0063 镥 -23.0064 铽 -23.0065 铕 -23.0066 钆 -23.0067 嗯 -23.0068 殑 -23.0069 铒 -23.007 钐 -23.0071 弑 -23.0072 ~ -23.0072 ° -23.0072 × -23.0072 ― -23.0072 ‘ -23.0072 → -23.0072 ♀ -23.0072 お -23.0072 ヤ -23.0072 ワ -23.0072 佹 -23.0072 勭 -23.0072 咱 -23.0072 堟 -23.0072 紝 -23.0072 臺 -23.0072 貔 -23.0072 鍓 -23.0072 鍙 -23.0072 鐨 -23.0072 镧 -23.0072 镨 -23.0072 呃 -23.0072 」 -23.0072 哂 -23.0072 pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/megatron/tokenizer/gpt2_tokenization.py000066400000000000000000000322231443434453700316150ustar00rootroot00000000000000# coding=utf-8 # Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization classes for OpenAI GPT.""" from __future__ import (absolute_import, division, print_function, unicode_literals) import sys import json import logging import os import regex as re from io import open try: from functools import lru_cache except ImportError: # Just a dummy decorator to get the checks to run on python2 # because honestly I don't want to support a byte-level unicode BPE # tokenizer on python 2 right now. def lru_cache(): return lambda func: func logger = logging.getLogger(__name__) PRETRAINED_VOCAB_ARCHIVE_MAP = { 'gpt2': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json", } PRETRAINED_MERGES_ARCHIVE_MAP = { 'gpt2': "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt", } PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = { 'gpt2': 1024, } VOCAB_NAME = 'vocab.json' MERGES_NAME = 'merges.txt' SPECIAL_TOKENS_NAME = 'special_tokens.txt' @lru_cache() def bytes_to_unicode(): """ Returns list of utf-8 byte and a corresponding list of unicode strings. The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. This is a signficant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup tables between utf-8 bytes and unicode strings. And avoids mapping to whitespace/control characters the bpe code barfs on. """ _chr = unichr if sys.version_info[0] == 2 else chr bs = list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + \ list(range(ord("®"), ord("ÿ") + 1)) cs = bs[:] n = 0 for b in range(2**8): if b not in bs: bs.append(b) cs.append(2**8 + n) n += 1 cs = [_chr(n) for n in cs] return dict(zip(bs, cs)) def get_pairs(word): """Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length strings). """ pairs = set() prev_char = word[0] for char in word[1:]: pairs.add((prev_char, char)) prev_char = char return pairs class GPT2Tokenizer(object): """ GPT-2 BPE tokenizer. Peculiarities: - Byte-level BPE """ @classmethod def from_pretrained(cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs): """ Instantiate a PreTrainedBertModel from a pre-trained model file. Download and cache the pre-trained model file if needed. """ if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP: vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name_or_path] merges_file = PRETRAINED_MERGES_ARCHIVE_MAP[pretrained_model_name_or_path] special_tokens_file = None else: vocab_file = os.path.join(pretrained_model_name_or_path, VOCAB_NAME) merges_file = os.path.join(pretrained_model_name_or_path, MERGES_NAME) special_tokens_file = os.path.join(pretrained_model_name_or_path, SPECIAL_TOKENS_NAME) if not os.path.exists(special_tokens_file): special_tokens_file = None else: logger.info("loading special tokens file {}".format(special_tokens_file)) # redirect to the cache, if necessary try: from .file_utils import cached_path resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir) resolved_merges_file = cached_path(merges_file, cache_dir=cache_dir) except EnvironmentError: logger.error( "Model name '{}' was not found in model name list ({}). " "We assumed '{}' was a path or url but couldn't find files {} and {} " "at this path or url.".format( pretrained_model_name_or_path, ', '.join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()), pretrained_model_name_or_path, vocab_file, merges_file)) return None if resolved_vocab_file == vocab_file and resolved_merges_file == merges_file: logger.info("loading vocabulary file {}".format(vocab_file)) logger.info("loading merges file {}".format(merges_file)) else: logger.info("loading vocabulary file {} from cache at {}".format( vocab_file, resolved_vocab_file)) logger.info("loading merges file {} from cache at {}".format( merges_file, resolved_merges_file)) if pretrained_model_name_or_path in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP: # if we're using a pretrained model, ensure the tokenizer wont index sequences longer # than the number of positional embeddings max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[pretrained_model_name_or_path] kwargs['max_len'] = min(kwargs.get('max_len', int(1e12)), max_len) # Instantiate tokenizer. if special_tokens_file and 'special_tokens' not in kwargs: special_tokens = open(special_tokens_file, encoding='utf-8').read().split('\n')[:-1] else: special_tokens = kwargs.pop('special_tokens', []) tokenizer = cls( resolved_vocab_file, resolved_merges_file, special_tokens=special_tokens, *inputs, **kwargs) return tokenizer def __init__(self, vocab_file, merges_file, errors='replace', special_tokens=None, max_len=None): self.max_len = max_len if max_len is not None else int(1e12) self.encoder = json.load(open(vocab_file)) self.decoder = {v: k for k, v in self.encoder.items()} self.errors = errors # how to handle errors in decoding self.byte_encoder = bytes_to_unicode() self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} bpe_data = open(merges_file, encoding='utf-8').read().split('\n')[1:-1] bpe_merges = [tuple(merge.split()) for merge in bpe_data] self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges)))) self.cache = {} # Should haved added re.IGNORECASE so BPE merges can happen for # capitalized versions of contractions self.pat = re.compile( r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""") self.special_tokens = {} self.special_tokens_decoder = {} self.set_special_tokens(special_tokens) def __len__(self): return len(self.encoder) + len(self.special_tokens) def set_special_tokens(self, special_tokens): """ Add a list of additional tokens to the encoder. The additional tokens are indexed starting from the last index of the current vocabulary in the order of the `special_tokens` list. """ if not special_tokens: self.special_tokens = {} self.special_tokens_decoder = {} return self.special_tokens = dict((tok, len(self.encoder) + i) for i, tok in enumerate(special_tokens)) self.special_tokens_decoder = {v: k for k, v in self.special_tokens.items()} logger.info("Special tokens {}".format(self.special_tokens)) def bpe(self, token): if token in self.cache: return self.cache[token] word = tuple(token) pairs = get_pairs(word) if not pairs: return token while True: bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf'))) if bigram not in self.bpe_ranks: break first, second = bigram new_word = [] i = 0 while i < len(word): try: j = word.index(first, i) new_word.extend(word[i:j]) i = j except BaseException: new_word.extend(word[i:]) break if word[i] == first and i < len(word) - 1 and word[i + 1] == second: new_word.append(first + second) i += 2 else: new_word.append(word[i]) i += 1 new_word = tuple(new_word) word = new_word if len(word) == 1: break else: pairs = get_pairs(word) word = ' '.join(word) self.cache[token] = word return word def tokenize(self, text): """ Tokenize a string. """ bpe_tokens = [] for token in re.findall(self.pat, text): if sys.version_info[0] == 2: token = ''.join(self.byte_encoder[ord(b)] for b in token) else: token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8')) bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(' ')) return bpe_tokens def convert_tokens_to_ids(self, tokens): """ Converts a sequence of tokens into ids using the vocab. """ ids = [] if isinstance(tokens, str) or (sys.version_info[0] == 2 and isinstance(tokens, unicode)): if tokens in self.special_tokens: return self.special_tokens[tokens] else: return self.encoder.get(tokens, 0) for token in tokens: if token in self.special_tokens: ids.append(self.special_tokens[token]) else: ids.append(self.encoder.get(token, 0)) if len(ids) > self.max_len: logger.warning( "Token indices sequence length is longer than the specified maximum " " sequence length for this OpenAI GPT model ({} > {}). Running this" " sequence through the model will result in indexing errors".format( len(ids), self.max_len) ) return ids def convert_ids_to_tokens(self, ids, skip_special_tokens=False): """Converts a sequence of ids in BPE tokens using the vocab.""" tokens = [] for i in ids: if i in self.special_tokens_decoder: if not skip_special_tokens: tokens.append(self.special_tokens_decoder[i]) else: tokens.append(self.decoder[i]) return tokens def encode(self, text): return self.convert_tokens_to_ids(self.tokenize(text)) def decode(self, tokens): text = ''.join([self.decoder[token] for token in tokens]) text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors=self.errors) return text def save_vocabulary(self, vocab_path): """Save the tokenizer vocabulary and merge files to a directory.""" if not os.path.isdir(vocab_path): logger.error("Vocabulary path ({}) should be a directory".format(vocab_path)) return vocab_file = os.path.join(vocab_path, VOCAB_NAME) merge_file = os.path.join(vocab_path, MERGES_NAME) special_tokens_file = os.path.join(vocab_path, SPECIAL_TOKENS_NAME) with open(vocab_file, 'w', encoding='utf-8') as f: f.write(json.dumps(self.encoder, ensure_ascii=False)) index = 0 with open(merge_file, "w", encoding="utf-8") as writer: writer.write(u'#version: 0.2\n') for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]): if index != token_index: logger.warning("Saving vocabulary to {}: BPE merge indices are not consecutive." " Please check that the tokenizer is not corrupted!".format(merge_file)) index = token_index writer.write(' '.join(bpe_tokens) + u'\n') index += 1 index = len(self.encoder) with open(special_tokens_file, 'w', encoding='utf-8') as writer: for token, token_index in sorted(self.special_tokens.items(), key=lambda kv: kv[1]): if index != token_index: logger.warning("Saving special tokens vocabulary to {}: BPE indices are not consecutive." " Please check that the tokenizer is not corrupted!".format(special_tokens_file)) index = token_index writer.write(token + u'\n') index += 1 return vocab_file, merge_file, special_tokens_file pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/megatron/tokenizer/tokenization_jieba.py000066400000000000000000000063311443434453700320140ustar00rootroot00000000000000# coding=utf-8 # Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization classes for OpenAI GPT.""" from __future__ import (absolute_import, division, print_function, unicode_literals) import sys import json import logging import os import regex as re from io import open import sentencepiece as spm import jieba try: from functools import lru_cache except ImportError: # Just a dummy decorator to get the checks to run on python2 # because honestly I don't want to support a byte-level unicode BPE tokenizer on python 2 right now. def lru_cache(): return lambda func: func class JIEBATokenizer(object): def __init__(self, vocab_file, max_len=None): self.max_len = max_len if max_len is not None else int(1e12) # self.encoder = json.load(open(vocab_file)) model_file = vocab_file + ".model" vocab_file = vocab_file + ".vocab" f = open(vocab_file,'r', encoding='utf-8') lines = f.readlines() self.encoder = {} for line in enumerate(lines): key = line[1].split('\t')[0] self.encoder[key] = line[0] self.decoder = {v:k for k,v in self.encoder.items()} self.sp = spm.SentencePieceProcessor(model_file=model_file) self.translator = str.maketrans(" \n", "\u2582\u2583") self.eod_id = self.encoder[''] self.eot_id = self.encoder[''] self.pad_id = self.encoder[''] self.unk = 0 @property def vocab_size(self): return len(self.encoder) def __len__(self): return len(self.encoder) + len(self.special_tokens) @property def eod(self): return self.eot_id def tokenize(self, text): """ Tokenize a string. """ seg_list = [x.translate(self.translator) for x in jieba.cut(text, cut_all=False)] new_seg = " ".join(seg_list) return self.sp.encode(new_seg) def convert_tokens_to_ids(self, tokens): return tokens def convert_ids_to_tokens(self, ids): return self.decode(ids) def detokenize(self, token_ids): return self.decode(token_ids) def encode(self, text): res = self.tokenize(text) return res def decode(self, tokens): text = self.sp.decode(tokens) text = text.replace(' ', '').replace('\u2582', ' ').replace('\u2583', '\n') return text if __name__ == '__main__': ids = [26, 80, 373, 5440, 2527, 1663, 11, 1268, 4896, 7902] vocab_file = '/userhome/pclproject/gpt/Megatron-LM-1.1-Pangu/megatron/tokenizer/bpe_4w_pcl/vocab' tokenizer = JIEBATokenizer(vocab_file) print(tokenizer.convert_ids_to_tokens(ids)) passpcl_pangu/pcl_pangu/model/panguAlpha_pytorch/megatron/tokenizer/tokenizer.py000066400000000000000000000147631443434453700301660ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Megatron tokenizers.""" from abc import ABC from abc import abstractmethod from .bert_tokenization import FullTokenizer as FullBertTokenizer from .gpt2_tokenization import GPT2Tokenizer from .tokenization_jieba import JIEBATokenizer def build_tokenizer(args): """Initialize tokenizer.""" if args.rank == 0: print('> building {} tokenizer ...'.format(args.tokenizer_type), flush=True) # Select and instantiate the tokenizer. assert args.vocab_file is not None if args.tokenizer_type == 'BertWordPieceLowerCase': tokenizer = _BertWordPieceTokenizer(vocab_file=args.vocab_file, lower_case=True) elif args.tokenizer_type == 'BertWordPieceCase': tokenizer = _BertWordPieceTokenizer(vocab_file=args.vocab_file, lower_case=False) elif args.tokenizer_type == 'GPT2BPETokenizer': # assert args.merge_file is not None # tokenizer = _GPT2BPETokenizer(args.vocab_file, args.merge_file) tokenizer = JIEBATokenizer(args.vocab_file) else: raise NotImplementedError('{} tokenizer is not ' 'implemented.'.format(args.tokenizer_type)) # Add vocab size. args.padded_vocab_size = _vocab_size_with_padding(tokenizer.vocab_size, args) return tokenizer def _vocab_size_with_padding(orig_vocab_size, args): """Pad vocab size so it is divisible by model parallel size and still having GPU friendly size.""" after = orig_vocab_size multiple = args.make_vocab_size_divisible_by * \ args.model_parallel_size while (after % multiple) != 0: after += 1 if args.rank == 0: print(' > padded vocab (size: {}) with {} dummy tokens ' '(new size: {})'.format( orig_vocab_size, after - orig_vocab_size, after), flush=True) return after class AbstractTokenizer(ABC): """Abstract class for tokenizer.""" def __init__(self, name): self.name = name super().__init__() @property @abstractmethod def vocab_size(self): pass @property @abstractmethod def vocab(self): """Dictionary from vocab text token to id token.""" pass @property @abstractmethod def inv_vocab(self): """Dictionary from vocab id token to text token.""" pass @abstractmethod def tokenize(self, text): pass def detokenize(self, token_ids): raise NotImplementedError('detokenizer is not implemented for {} ' 'tokenizer'.format(self.name)) @property def cls(self): raise NotImplementedError('CLS is not provided for {} ' 'tokenizer'.format(self.name)) @property def sep(self): raise NotImplementedError('SEP is not provided for {} ' 'tokenizer'.format(self.name)) @property def pad(self): raise NotImplementedError('PAD is not provided for {} ' 'tokenizer'.format(self.name)) @property def eod(self): raise NotImplementedError('EOD is not provided for {} ' 'tokenizer'.format(self.name)) @property def mask(self): raise NotImplementedError('MASK is not provided for {} ' 'tokenizer'.format(self.name)) class _BertWordPieceTokenizer(AbstractTokenizer): """Original BERT wordpiece tokenizer.""" def __init__(self, vocab_file, lower_case=True): if lower_case: name = 'BERT Lower Case' else: name = 'BERT Upper Case' super().__init__(name) self.tokenizer = FullBertTokenizer(vocab_file, do_lower_case=lower_case) self.cls_id = self.tokenizer.vocab['[CLS]'] self.sep_id = self.tokenizer.vocab['[SEP]'] self.pad_id = self.tokenizer.vocab['[PAD]'] self.mask_id = self.tokenizer.vocab['[MASK]'] @property def vocab_size(self): return self.tokenizer.vocab_size() @property def vocab(self): return self.tokenizer.vocab @property def inv_vocab(self): return self.tokenizer.inv_vocab def tokenize(self, text): text_tokens = self.tokenizer.tokenize(text) return self.tokenizer.convert_tokens_to_ids(text_tokens) def decode_token_ids(self, token_ids): tokens = self.tokenizer.convert_ids_to_tokens(token_ids) exclude_list = ['[PAD]', '[CLS]'] non_pads = [t for t in tokens if t not in exclude_list] result = "" for s in non_pads: if s.startswith("##"): result += s[2:] else: result += " " + s return result @property def cls(self): return self.cls_id @property def sep(self): return self.sep_id @property def pad(self): return self.pad_id @property def mask(self): return self.mask_id class _GPT2BPETokenizer(AbstractTokenizer): """Original GPT2 BPE tokenizer.""" def __init__(self, vocab_file, merge_file): name = 'GPT2 BPE' super().__init__(name) self.tokenizer = GPT2Tokenizer(vocab_file, merge_file, errors='replace', special_tokens=[], max_len=None) self.eod_id = self.tokenizer.encoder['<|endoftext|>'] @property def vocab_size(self): return len(self.tokenizer.encoder) @property def vocab(self): return self.tokenizer.encoder @property def inv_vocab(self): return self.tokenizer.decoder def tokenize(self, text): return self.tokenizer.encode(text) def detokenize(self, token_ids): return self.tokenizer.decode(token_ids) @property def eod(self): return self.eod_id pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/megatron/training.py000066400000000000000000000516541443434453700257550ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Pretrain utilities.""" from datetime import datetime import math import sys import torch from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP from apex.optimizers import FusedAdam as Adam from megatron import get_args from megatron import get_timers from megatron import get_tensorboard_writer from megatron import mpu from megatron import print_rank_0 from megatron.checkpointing import load_checkpoint from megatron.checkpointing import save_checkpoint from megatron.fp16 import FP16_Module from megatron.fp16 import FP16_Optimizer from megatron.initialize import initialize_megatron from megatron.learning_rates import AnnealingLR from megatron.model import DistributedDataParallel as LocalDDP from megatron.model import get_params_for_weight_decay_optimization from megatron.model.realm_model import ICTBertModel from megatron.utils import check_adlr_autoresume_termination from megatron.utils import make_data_loader from megatron.utils import report_memory def pretrain(train_valid_test_dataset_provider, model_provider, forward_step_func, extra_args_provider=None, args_defaults={}): """Main training program. This function will run the followings in the order provided: 1) initialize Megatron. 2) setup model, optimizer and lr schedule using the model_provider. 3) call train_val_test_data_provider to get train/val/test datasets. 4) train the modle using the forward_step_func. Arguments: train_valid_test_dataset_provider: a function that takes the size of train/valid/test dataset and returns `train, valid, test` datasets. model_provider: a function that returns a vanilla version of the model. By vanilla we mean a simple model on cpu with no fp16 or ddp. forward_step_func: a function that takes a `data iterator` and `model`, and returns a `loss` scalar with a dictionary with key:values being the info we would like to monitor during training, for example `lm-loss: value`. We also require that this function add `batch generator` to the timers class. extra_args_provider: a function that takes a parser and adds arguments to it. It is used for programs to add their own arguments. args_defaults: a dictionary from argument-name to argument-value. It to set already parse arguments. """ # Initalize and get arguments, timers, and Tensorboard writer. initialize_megatron(extra_args_provider=extra_args_provider, args_defaults=args_defaults) args = get_args() timers = get_timers() # Model, optimizer, and learning rate. timers('model and optimizer').start() model, optimizer, lr_scheduler = setup_model_and_optimizer(model_provider) timers('model and optimizer').stop() # Data stuff. timers('train/valid/test data iterators').start() train_data_iterator, valid_data_iterator, test_data_iterator \ = build_train_valid_test_data_iterators( train_valid_test_dataset_provider) timers('train/valid/test data iterators').stop() # Print setup timing. print_rank_0('done with setups ...') timers.log(['model and optimizer', 'train/valid/test data iterators']) print_rank_0('training ...') iteration = 0 if args.do_train and args.train_iters > 0: iteration, _ = train(forward_step_func, model, optimizer, lr_scheduler, train_data_iterator, valid_data_iterator) if args.do_valid: prefix = 'the end of training for val data' evaluate_and_print_results(prefix, forward_step_func, valid_data_iterator, model, iteration, False) if args.save and iteration != 0: save_checkpoint(iteration, model, optimizer, lr_scheduler) if args.do_test: # Run on test data. prefix = 'the end of training for test data' evaluate_and_print_results(prefix, forward_step_func, test_data_iterator, model, 0, True) def get_model(model_provider_func): """Build the model.""" args = get_args() # Build model on cpu. model = model_provider_func() # Print number of parameters. if mpu.get_data_parallel_rank() == 0: print(' > number of parameters on model parallel rank {}: {}'.format( mpu.get_model_parallel_rank(), sum([p.nelement() for p in model.parameters()])), flush=True) # GPU allocation. model.cuda(torch.cuda.current_device()) # Fp16 conversion. if args.fp16: model = FP16_Module(model) # Wrap model for distributed training.""" if args.DDP_impl == 'torch': i = torch.cuda.current_device() model = torchDDP(model, device_ids=[i], output_device=i, process_group=mpu.get_data_parallel_group()) return model if args.DDP_impl == 'local': model = LocalDDP(model) return model raise NotImplementedError('Unknown DDP implementation specified: {}. ' 'Exiting.'.format(args.DDP_impl)) def get_optimizer(model): """Set up the optimizer.""" args = get_args() # Build parameter groups (weight decay and non-decay). while isinstance(model, (torchDDP, LocalDDP, FP16_Module)): model = model.module param_groups = get_params_for_weight_decay_optimization(model) # Add model parallel attribute if it is not set. for param_group in param_groups: for param in param_group['params']: if not hasattr(param, 'model_parallel'): param.model_parallel = False # Use Adam. optimizer = Adam(param_groups, lr=args.lr, weight_decay=args.weight_decay, betas=(0.9, 0.95), eps=1e-8) # Wrap into fp16 optimizer. if args.fp16: optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale, dynamic_loss_scale=args.dynamic_loss_scale, dynamic_loss_args={ 'scale_window': args.loss_scale_window, 'min_scale': args.min_scale, 'delayed_shift': args.hysteresis}) return optimizer def get_learning_rate_scheduler(optimizer): """Build the learning rate scheduler.""" args = get_args() # Add linear learning rate scheduler. if args.lr_decay_iters is not None: num_iters = args.lr_decay_iters else: num_iters = args.train_iters num_iters = max(1, num_iters) init_step = 0 warmup_iter = args.warmup * num_iters lr_scheduler = AnnealingLR( optimizer, start_lr=args.lr, warmup_iter=warmup_iter, total_iters=num_iters, decay_style=args.lr_decay_style, last_iter=init_step, min_lr=args.min_lr, use_checkpoint_lr_scheduler=args.use_checkpoint_lr_scheduler, override_lr_scheduler=args.override_lr_scheduler) return lr_scheduler def setup_model_and_optimizer(model_provider_func): """Setup model and optimizer.""" args = get_args() model = get_model(model_provider_func) optimizer = get_optimizer(model) lr_scheduler = get_learning_rate_scheduler(optimizer) if args.load is not None: args.iteration = load_checkpoint(model, optimizer, lr_scheduler) else: args.iteration = 0 # get model without FP16 and/or TorchDDP wrappers unwrapped_model = model while hasattr(unwrapped_model, 'module'): unwrapped_model = unwrapped_model.module if args.iteration == 0 and hasattr(unwrapped_model, 'init_state_dict_from_bert'): print("Initializing ICT from pretrained BERT model", flush=True) unwrapped_model.init_state_dict_from_bert() return model, optimizer, lr_scheduler def backward_step(optimizer, model, loss): """Backward step.""" args = get_args() timers = get_timers() # Backward pass. timers('backward-backward').start() if args.fp16: optimizer.zero_grad(set_grads_to_None=True) else: optimizer.zero_grad() if args.fp16: optimizer.backward(loss, update_master_grads=False) else: loss.backward() timers('backward-backward').stop() # All-reduce if needed. if args.DDP_impl == 'local': timers('backward-allreduce').start() model.allreduce_params(reduce_after=False, fp32_allreduce=args.fp32_allreduce) timers('backward-allreduce').stop() # Update master gradients. timers('backward-master-grad').start() if args.fp16: optimizer.update_master_grads() timers('backward-master-grad').stop() # Clipping gradients helps prevent the exploding gradient. timers('backward-clip-grad').start() if args.clip_grad > 0: if not args.fp16: mpu.clip_grad_norm(model.parameters(), args.clip_grad) else: optimizer.clip_master_grads(args.clip_grad) timers('backward-clip-grad').stop() def train_step(forward_step_func, data_iterator, model, optimizer, lr_scheduler): """Single training step.""" args = get_args() timers = get_timers() # Forward model for one step. timers('forward').start() loss, loss_reduced = forward_step_func(data_iterator, model) timers('forward').stop() # Calculate gradients, reduce across processes, and clip. timers('backward').start() backward_step(optimizer, model, loss) timers('backward').stop() # Update parameters. timers('optimizer').start() optimizer.step() timers('optimizer').stop() # Update learning rate. skipped_iter = 0 if not (args.fp16 and optimizer.overflow): lr_scheduler.step() else: skipped_iter = 1 return loss_reduced, skipped_iter def training_log(loss_dict, total_loss_dict, learning_rate, iteration, loss_scale, report_memory_flag): """Log training information such as losses, timing, ....""" args = get_args() timers = get_timers() writer = get_tensorboard_writer() # Update losses. for key in loss_dict: total_loss_dict[key] = total_loss_dict.get(key, 0.) + loss_dict[key] # Logging. timers_to_log = [] def add_to_logging(name): if name in timers.timers: timers_to_log.append(name) add_to_logging('forward') add_to_logging('backward') add_to_logging('backward-backward') add_to_logging('backward-allreduce') add_to_logging('backward-master-grad') add_to_logging('backward-clip-grad') add_to_logging('optimizer') add_to_logging('batch generator') # Tensorboard values. if writer and torch.distributed.get_rank() == 0: writer.add_scalar('learning_rate', learning_rate, iteration) for key in loss_dict: writer.add_scalar(key, loss_dict[key], iteration) if args.fp16: writer.add_scalar('loss_scale', loss_scale, iteration) normalizer = iteration % args.log_interval if normalizer == 0: normalizer = args.log_interval timers.write(timers_to_log, writer, iteration, normalizer=normalizer) if iteration % args.log_interval == 0: elapsed_time = timers('interval time').elapsed() if writer and torch.distributed.get_rank() == 0: writer.add_scalar('iteration_time', elapsed_time / args.log_interval, iteration) log_string = ' iteration {:8d}/{:8d} |'.format(iteration, args.train_iters) log_string += ' elapsed time per iteration (ms): {:.1f} |'.format( elapsed_time * 1000.0 / args.log_interval) log_string += ' learning rate: {:.3E} |'.format(learning_rate) for key in total_loss_dict: avg = total_loss_dict[key].item() / args.log_interval log_string += ' {}: {:.6E} |'.format(key, avg) total_loss_dict[key] = 0.0 if args.fp16: log_string += ' loss scale: {:.1f} |'.format(loss_scale) print_rank_0(log_string) if report_memory_flag: report_memory('after {} iterations'.format(iteration)) report_memory_flag = False timers.log(timers_to_log, normalizer=args.log_interval) return report_memory_flag def train(forward_step_func, model, optimizer, lr_scheduler, train_data_iterator, valid_data_iterator): """Train the model function.""" args = get_args() timers = get_timers() # Turn on training mode which enables dropout. model.train() # Tracking loss. total_loss_dict = {} # Iterations. iteration = args.iteration skipped_iters = 0 timers('interval time').start() report_memory_flag = True while iteration < args.train_iters: loss_dict, skipped_iter = train_step(forward_step_func, train_data_iterator, model, optimizer, lr_scheduler) skipped_iters += skipped_iter iteration += 1 # Logging. loss_scale = None if args.fp16: loss_scale = optimizer.loss_scale report_memory_flag = training_log(loss_dict, total_loss_dict, optimizer.param_groups[0]['lr'], iteration, loss_scale, report_memory_flag) # Autoresume if args.adlr_autoresume and \ (iteration % args.adlr_autoresume_interval == 0): check_adlr_autoresume_termination(iteration, model, optimizer, lr_scheduler) # Checkpointing if args.save and args.save_interval and \ iteration % args.save_interval == 0: save_checkpoint(iteration, model, optimizer, lr_scheduler) # Evaluation if args.eval_interval and iteration % args.eval_interval == 0 and \ args.do_valid: prefix = 'iteration {}'.format(iteration) evaluate_and_print_results(prefix, forward_step_func, valid_data_iterator, model, iteration, False) if args.exit_interval and iteration % args.exit_interval == 0: torch.distributed.barrier() time_str = datetime.now().strftime('%Y-%m-%d %H:%M:%S') rank = torch.distributed.get_rank() print_rank_0('rank: {} | time: {} | exiting the program at iteration {}'.format(rank, time_str, iteration)) sys.exit() return iteration, skipped_iters def evaluate(forward_step_func, data_iterator, model, verbose=False): """Evaluation.""" args = get_args() # Turn on evaluation mode which disables dropout. model.eval() total_loss_dict = {} with torch.no_grad(): iteration = 0 while iteration < args.eval_iters: iteration += 1 if verbose and iteration % args.log_interval == 0: print_rank_0('Evaluating iter {}/{}'.format(iteration, args.eval_iters)) # Forward evaluation. _, loss_dict = forward_step_func(data_iterator, model) # Reduce across processes. for key in loss_dict: total_loss_dict[key] = total_loss_dict.get(key, 0.) + \ loss_dict[key] # Move model back to the train mode. model.train() for key in total_loss_dict: total_loss_dict[key] /= args.eval_iters return total_loss_dict def evaluate_and_print_results(prefix, forward_step_func, data_iterator, model, iteration, verbose=False): """Helper function to evaluate and dump results on screen.""" writer = get_tensorboard_writer() total_loss_dict = evaluate(forward_step_func, data_iterator, model, verbose) string = ' validation loss at {} | '.format(prefix) for key in total_loss_dict: string += '{} value: {:.6E} | '.format(key, total_loss_dict[key].item()) ppl = math.exp(min(20, total_loss_dict[key].item())) string += '{} PPL: {:.6E} | '.format(key, ppl) if writer and torch.distributed.get_rank() == 0: writer.add_scalar('{} value'.format(key), total_loss_dict[key].item(), iteration) writer.add_scalar('{} ppl'.format(key), ppl, iteration) length = len(string) + 1 print_rank_0('-' * length) print_rank_0(string) print_rank_0('-' * length) def build_train_valid_test_data_iterators( build_train_valid_test_datasets_provider): """XXX""" args = get_args() (train_dataloader, valid_dataloader, test_dataloader) = (None, None, None) print_rank_0('> building train, validation, and test datasets ...') # Data loader only on rank 0 of each model parallel group. if mpu.get_model_parallel_rank() == 0: # Rank, size, and global batch size. data_parallel_size = mpu.get_data_parallel_world_size() global_batch_size = args.batch_size * data_parallel_size # Number of train/valid/test samples. train_iters = args.train_iters eval_iters = (train_iters // args.eval_interval + 1) * args.eval_iters test_iters = args.eval_iters train_val_test_num_samples = [train_iters * global_batch_size, eval_iters * global_batch_size, test_iters * global_batch_size] print_rank_0(' > datasets target sizes (minimum size):') print_rank_0(' train: {}'.format(train_val_test_num_samples[0])) print_rank_0(' validation: {}'.format(train_val_test_num_samples[1])) print_rank_0(' test: {}'.format(train_val_test_num_samples[2])) # Build the datasets. train_ds, valid_ds, test_ds = build_train_valid_test_datasets_provider( train_val_test_num_samples) # Build dataloders. train_dataloader = make_data_loader(train_ds) valid_dataloader = make_data_loader(valid_ds) test_dataloader = make_data_loader(test_ds) # Flags to know if we need to do training/validation/testing. do_train = train_dataloader is not None and args.train_iters > 0 do_valid = valid_dataloader is not None and args.eval_iters > 0 do_test = test_dataloader is not None and args.eval_iters > 0 # Need to broadcast num_tokens and num_type_tokens. flags = torch.cuda.LongTensor( [int(do_train), int(do_valid), int(do_test)]) else: flags = torch.cuda.LongTensor([0, 0, 0]) # Broadcast num tokens. torch.distributed.broadcast(flags, mpu.get_model_parallel_src_rank(), group=mpu.get_model_parallel_group()) args.do_train = flags[0].item() args.do_valid = flags[1].item() args.do_test = flags[2].item() # Shift the start iterations. if train_dataloader is not None: train_dataloader.batch_sampler.start_iter = args.iteration % \ len(train_dataloader) print_rank_0('setting training data start iteration to {}'. format(train_dataloader.batch_sampler.start_iter)) if valid_dataloader is not None: start_iter_val = (args.iteration // args.eval_interval) * \ args.eval_iters valid_dataloader.batch_sampler.start_iter = start_iter_val % \ len(valid_dataloader) print_rank_0('setting validation data start iteration to {}'. format(valid_dataloader.batch_sampler.start_iter)) # Build iterators. if train_dataloader is not None: train_data_iterator = iter(train_dataloader) else: train_data_iterator = None if valid_dataloader is not None: valid_data_iterator = iter(valid_dataloader) else: valid_data_iterator = None if test_dataloader is not None: test_data_iterator = iter(test_dataloader) else: test_data_iterator = None return train_data_iterator, valid_data_iterator, test_data_iterator pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/megatron/utils.py000066400000000000000000000147111443434453700252730ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """General utilities.""" import sys import torch from megatron import get_args from megatron import print_rank_0 from megatron import get_adlr_autoresume from megatron import mpu from megatron.checkpointing import save_checkpoint from megatron.data.samplers import DistributedBatchSampler from megatron.fp16 import FP16_Optimizer def reduce_losses(losses): """Reduce a tensor of losses across all GPUs.""" reduced_losses = torch.cat( [loss.clone().detach().view(1) for loss in losses]) torch.distributed.all_reduce(reduced_losses) reduced_losses = reduced_losses / torch.distributed.get_world_size() return reduced_losses def report_memory(name): """Simple GPU memory report.""" mega_bytes = 1024.0 * 1024.0 string = name + ' memory (MB)' string += ' | allocated: {}'.format( torch.cuda.memory_allocated() / mega_bytes) string += ' | max allocated: {}'.format( torch.cuda.max_memory_allocated() / mega_bytes) string += ' | reserved: {}'.format(torch.cuda.memory_reserved() / mega_bytes) string += ' | max reserved: {}'.format( torch.cuda.max_memory_reserved() / mega_bytes) print_rank_0(string) def print_params_min_max_norm(optimizer, iteration): """Print min, max, and norm of all parameters.""" index = 0 rank = torch.distributed.get_rank() string = 'iteration, rank, index, model-parallel,min, max, norm\n' optimizer_ = optimizer if isinstance(optimizer, FP16_Optimizer): optimizer_ = optimizer.optimizer for param_group in optimizer_.param_groups: for param in param_group['params']: index += 1 min_ = param.data.min() max_ = param.data.max() norm = param.data.norm() string += '{:7d}, {:4d}, {:4d}, {:2d}, '.format( iteration, rank, index, int(param.model_parallel)) string += '{:.6E}, {:.6E}, {:.6E}\n'.format(min_, max_, norm) print(string, flush=True) def check_adlr_autoresume_termination(iteration, model, optimizer, lr_scheduler): """Check for autoresume signal and exit if it is received.""" args = get_args() autoresume = get_adlr_autoresume() # Add barrier to ensure consistnecy. torch.distributed.barrier() if autoresume.termination_requested(): if args.save: save_checkpoint(iteration, model, optimizer, lr_scheduler) print_rank_0(">>> autoresume termination request found!") if torch.distributed.get_rank() == 0: autoresume.request_resume() print_rank_0(">>> training terminated. Returning") sys.exit(0) def make_data_loader(dataset): """Buld dataloader given an input dataset.""" if dataset is None: return None args = get_args() # Data parallel arguments. world_size = mpu.get_data_parallel_world_size() rank = mpu.get_data_parallel_rank() global_batch_size = args.batch_size * world_size num_workers = args.num_workers # Use a simple sampler with distributed batch sampler. sampler = torch.utils.data.SequentialSampler(dataset) batch_sampler = DistributedBatchSampler(sampler=sampler, batch_size=global_batch_size, drop_last=True, rank=rank, world_size=world_size) # Torch dataloader. return torch.utils.data.DataLoader(dataset, batch_sampler=batch_sampler, num_workers=num_workers, pin_memory=True) def get_ltor_masks_and_position_ids(data, eod_token, reset_position_ids, reset_attention_mask, eod_mask_loss): """Build masks and position id for left to right model.""" # Extract batch size and sequence length. batch_size, seq_length = data.size() # Attention mask (lower triangular). if reset_attention_mask: att_mask_batch = batch_size else: att_mask_batch = 1 attention_mask = torch.tril(torch.ones( (att_mask_batch, seq_length, seq_length), device=data.device)).view( att_mask_batch, 1, seq_length, seq_length) # Loss mask. loss_mask = torch.ones(data.size(), dtype=torch.float, device=data.device) if eod_mask_loss: loss_mask[data == eod_token] = 0.0 # Position ids. position_ids = torch.arange(seq_length, dtype=torch.long, device=data.device) position_ids = position_ids.unsqueeze(0).expand_as(data) # We need to clone as the ids will be modifed based on batch index. if reset_position_ids: position_ids = position_ids.clone() if reset_position_ids or reset_attention_mask: # Loop through the batches: for b in range(batch_size): # Find indecies where EOD token is. eod_index = position_ids[b, data[b] == eod_token] # Detach indecies from positions if going to modify positions. if reset_position_ids: eod_index = eod_index.clone() # Loop through EOD indecies: prev_index = 0 for j in range(eod_index.size()[0]): i = eod_index[j] # Mask attention loss. if reset_attention_mask: attention_mask[b, 0, (i + 1):, :(i + 1)] = 0 # Reset positions. if reset_position_ids: position_ids[b, (i + 1):] -= (i + 1 - prev_index) prev_index = i + 1 # Convert attention mask to binary: attention_mask = (attention_mask < 0.5) return attention_mask, loss_mask, position_ids pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/mergeMpCkpt.py000066400000000000000000000544121443434453700245370ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Merge model parallel partitions.""" import os import sys from megatron.initialize import initialize_megatron sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir))) import numpy as np import torch from megatron import mpu from megatron.checkpointing import ensure_directory_exists from megatron.checkpointing import get_checkpoint_name from megatron.checkpointing import get_checkpoint_tracker_filename from megatron.global_vars import rebuild_tokenizer from megatron.global_vars import _parse_args # os.environ["CUDA_VISIBLE_DEVICES"] = "4,5,6,7" # os.system("export CUDA_VISIBLE_DEVICES=4,5,6,7") ######################### # --model-type GPT2 --model-parallel-size 2 --tokenizer-type GPT2BPETokenizer --vocab-file /ghome/yands/pclproject/gpt/Megatron-LM-1.1/megatron/tokenizer/bpe_4w_pcl/vocab --num-layers 24 --hidden-size 1024 --num-attention-heads 16 --seq-length 1024 --max-position-embeddings 1024 --load /ghome/yands/model/checkPoints/megatron-1.1-pangu # --model-type GPT2 --model-parallel-size 4 --tokenizer-type GPT2BPETokenizer --vocab-file /ghome/yands/pclproject/gpt/Megatron-LM-1.1/megatron/tokenizer/bpe_4w_pcl/vocab --num-layers 31 --hidden-size 2560 --num-attention-heads 32 --seq-length 1024 --max-position-embeddings 1024 --load /ghome/yands/model/checkPoints/megatron-1.1-pangu-2.6B ######################### def split_into_partitions(tensor, num_partitions, partition_dim, stride): per_partition_size = mpu.utils.divide(tensor.size(partition_dim), num_partitions) per_partition_per_stride_size = mpu.utils.divide(per_partition_size, stride) partitions_list = torch.split(tensor, per_partition_per_stride_size, dim=partition_dim) partitions = [] for i in range(num_partitions): partition = torch.cat(partitions_list[i::num_partitions], dim=partition_dim) partitions.append(partition) return partitions def merge_partitions(merged, partitions, partition_dim, stride): # Number and size of each partition. num_partitions = len(partitions) per_partition_size = None for partition in partitions: if per_partition_size is None: per_partition_size = partition.size(partition_dim) else: assert per_partition_size == partition.size(partition_dim) def concat_partitions(partitions_): with torch.no_grad(): if (per_partition_size * num_partitions) == merged.size( partition_dim): torch.cat(partitions_, dim=partition_dim, out=merged) else: print(' ***WARNING*** sizes do not match. Will cut ' 'the merged partitions by {} along dimension {} ' 'to reduce the size from {} to {} ...'.format( (per_partition_size * num_partitions) - \ merged.size(partition_dim), partition_dim, per_partition_size * num_partitions, merged.size(partition_dim))) merged_ = torch.cat(partitions_, dim=partition_dim) merged_split = torch.split(merged_, merged.size(partition_dim), dim=partition_dim) merged_ = merged_split[0] assert merged_.size(partition_dim) == merged.size(partition_dim) merged.data.copy_(merged_.data) # If stride is 1, then do simple concatination. if stride == 1: concat_partitions(partitions) return # For none unity strides, first split based on stride and then group. per_partition_per_stride_size = mpu.utils.divide(per_partition_size, stride) # Chunk and build a list. chunks = None for i, partition in enumerate(partitions): chunk = torch.split(partition, per_partition_per_stride_size, dim=partition_dim) if chunks is None: chunks = [0]*(num_partitions*len(chunk)) chunks[i::num_partitions] = chunk # Concatinate. concat_partitions(chunks) return def get_model(model_type): if model_type == 'BERT': from pretrain_bert import model_provider elif model_type == 'GPT2': from pretrain_gpt2 import model_provider elif model_type == 'RACE': from tasks.race.finetune import model_provider elif model_type == ['MNLI', 'QQP']: num_classes = 2 if model_type == 'MNLI': num_classes = 3 from megatron.model.classification import Classification def model_provider(): return Classification(num_classes=num_classes, num_tokentypes=2) else: raise Exception('unrecognized model type: {}'.format(model_type)) model = model_provider() model = model.half() return model def get_parallel_checkpoint_name(path): tracker_filename = get_checkpoint_tracker_filename(path) iteration = 0 with open(tracker_filename, 'r') as f: metastring = f.read().strip() iteration = int(metastring) assert iteration > 0 checkpoint_name = get_checkpoint_name(path, iteration) return checkpoint_name, iteration def test_split_merge(): print('testing split and merge ...') #[QKV.ROW-COL] tensor = torch.FloatTensor([[1.11, 1.12, 1.13, 1.14, 1.15], [1.21, 1.22, 1.23, 1.24, 1.25], [1.31, 1.32, 1.33, 1.34, 1.35], [1.41, 1.42, 1.43, 1.44, 1.45], [2.11, 2.12, 2.13, 2.14, 2.15], [2.21, 2.22, 2.23, 2.24, 2.25], [2.31, 2.32, 2.33, 2.34, 2.35], [2.41, 2.42, 2.43, 2.44, 2.45], [3.11, 3.12, 3.13, 3.14, 3.15], [3.21, 3.22, 3.23, 3.24, 3.25], [3.31, 3.32, 3.33, 3.34, 3.35], [3.41, 3.42, 3.43, 3.44, 3.45]]) num_partitions = 2 partition_dim = 0 stride = 1 partitions = split_into_partitions(tensor, num_partitions, partition_dim, stride) merged = torch.zeros_like(tensor) merge_partitions(merged, partitions, partition_dim, stride) max_error = (merged - tensor).abs().max() print(' > max error (should be zero): {}'.format(max_error)) def get_mp_merge_args(parser): """Provide extra arguments required for merging.""" group = parser.add_argument_group(title='mp merge') group.add_argument('--model-type', type=str, required=True, choices=['BERT', 'GPT2', 'RACE', 'MNLI', 'QQP'], help='Type of the mdoel.') return parser def main(): # test_split_merge() # Args args = _parse_args(extra_args_provider=get_mp_merge_args) model_type = args.model_type # orig_model_parallel_size = args.model_parallel_size orig_model_parallel_size = 4 args.model_parallel_size = 1 tokenizer = rebuild_tokenizer(args) print('\n merging model parallel partitions ...') print(' > number of partitions: {}'.format(orig_model_parallel_size)) print(' > checkpoint path: {}'.format(args.load)) print(' > model parameters:') print(' number of tokens ................ {} '.format( tokenizer.vocab_size)) print(' number of layers ................ {}'.format(args.num_layers)) print(' hidden sise ..................... {}'.format(args.hidden_size)) print(' number of attention heads ....... {}'.format( args.num_attention_heads)) print(' maximum position embeddings ..... {}'.format( args.max_position_embeddings)) # Full model. print('> building the full model ...') mpu.initialize.set_model_parallel_world_size(1) mpu.initialize.set_model_parallel_rank(0) # initialize_megatron(args_defaults={'tokenizer_type': 'GPT2BPETokenizer'}) merged_model = get_model(model_type) # Build and load partitions. partitions = [] iteration = 0 args.model_parallel_size = orig_model_parallel_size tokenizer = rebuild_tokenizer(args) mpu.initialize.set_model_parallel_world_size(args.model_parallel_size) for rank in range(args.model_parallel_size): mpu.initialize.set_model_parallel_rank(rank) checkpoint_name, iteration = get_parallel_checkpoint_name(args.load) print('> loading {} ...'.format(checkpoint_name)) model_ = get_model(model_type) sd = torch.load(checkpoint_name, map_location='cpu') # model_.load_state_dict(sd['model']) partitions.append(model_) # Parameter generators so we can loop through them semiltaneouly. merged_params_gen = merged_model.named_parameters() partitions_params_gen = [partition.named_parameters() for partition in partitions] while True: try: # Get the params and check names. name, merged_param = next(merged_params_gen) print(' > working on {} ...'.format(name)) print(' merged type: {}, size: {}'.format( merged_param.dtype, list(merged_param.size()))) partitions_param = [] for rank, partition_params_gen in enumerate(partitions_params_gen): partition_name, partition_param = next(partition_params_gen) assert partition_name == name partitions_param.append(partition_param) print(' partition {} type: {}, size: {}'.format( rank, partition_param.dtype, list(partition_param.size()))) # For the non-parallel parameters, simply copy the rank 0 values. if not hasattr(merged_param, 'model_parallel'): print(' none-parallel parameter, simple copy from rank 0') with torch.no_grad(): merged_param.data.copy_(partitions_param[0].data) # For parallel parameters, merge the values else: print(' parallel parameter merge with stride {} along ' 'dimention {}'.format(merged_param.stride, merged_param.partition_dim)) merge_partitions(merged_param, partitions_param, merged_param.partition_dim, merged_param.stride) except StopIteration: break # Save the model. args.model_parallel_size = 1 mpu.initialize.set_model_parallel_rank(0) sd = {} sd['model'] = merged_model.state_dict_for_save_checkpoint() sd['iteration'] = iteration merged_path = os.path.join(args.load, 'merged_v2') checkpoint_name = get_checkpoint_name(merged_path, iteration) ensure_directory_exists(checkpoint_name) loadModelFromNp(sd, args.num_layers) print('> saving merged model to {}'.format(checkpoint_name)) torch.save(sd, checkpoint_name) print('done :-)') def loadModelFromNp(sd, num_layers): npCkptPath = '/userhome/model/panguAlphaNumpyCkpt/' languageModel = sd['model']['language_model'] loadEmbeddingFromNp(npCkptPath, languageModel) transformer = sd['model']['language_model']['transformer'] for layerID in range(num_layers): loadAttentionLayerFromNp(npCkptPath, transformer, layerID) loadQueryLayerFromNp(npCkptPath, transformer) transformer['final_layernorm.weight'][:] = \ torch.tensor( np.load(npCkptPath+f'backbone.layernorm.gamma.npy') ).float() transformer['final_layernorm.bias'][:] = \ torch.tensor( np.load(npCkptPath+f'backbone.layernorm.beta.npy') ).float() def loadEmbeddingFromNp(npCkptPath, languageModel, vocabSize=40000): word_embedding_np = \ np.load(npCkptPath+'backbone.word_embedding.embedding_table.npy') languageModel['embedding']['word_embeddings']['weight'][:vocabSize,:] = \ torch.tensor(word_embedding_np).float() position_embeddings_np = \ np.load(npCkptPath+'backbone.position_embedding.embedding_table.npy') languageModel['embedding']['position_embeddings']['weight'][:,:] = \ torch.tensor(position_embeddings_np).float() topQueryEmbedding_np = \ np.load(npCkptPath+'backbone.top_query_embedding.embedding_table.npy') languageModel['topQueryEmbedding']['top_query_embeddings']['weight'][:,:] = \ torch.tensor(topQueryEmbedding_np).float() def loadAttentionLayerFromNp(npCkptPath, transformer, layerID): attention_dense1_weight_np = \ np.load(npCkptPath+f'backbone.blocks.{layerID}.attention.dense1.weight.npy') attention_dense2_weight_np = \ np.load(npCkptPath+f'backbone.blocks.{layerID}.attention.dense2.weight.npy') attention_dense3_weight_np = \ np.load(npCkptPath+f'backbone.blocks.{layerID}.attention.dense3.weight.npy') attention_dense1_bias_np = \ np.load(npCkptPath+f'backbone.blocks.{layerID}.attention.dense1.bias.npy') attention_dense2_bias_np = \ np.load(npCkptPath+f'backbone.blocks.{layerID}.attention.dense2.bias.npy') attention_dense3_bias_np = \ np.load(npCkptPath+f'backbone.blocks.{layerID}.attention.dense3.bias.npy') query_weight = transformer[f'layers.{layerID}.attention.query.weight'] key_weight = transformer[f'layers.{layerID}.attention.key.weight'] value_weight = transformer[f'layers.{layerID}.attention.value.weight'] query_weight[:] = torch.tensor(attention_dense1_weight_np).float() key_weight[:] = torch.tensor(attention_dense2_weight_np).float() value_weight[:] = torch.tensor(attention_dense3_weight_np).float() query_bias = transformer[f'layers.{layerID}.attention.query.bias'] key_bias = transformer[f'layers.{layerID}.attention.key.bias'] value_bias = transformer[f'layers.{layerID}.attention.value.bias'] query_bias[:] = torch.tensor(attention_dense1_bias_np).float() key_bias[:] = torch.tensor(attention_dense2_bias_np).float() value_bias[:] = torch.tensor(attention_dense3_bias_np).float() att_dense_weight = transformer[f'layers.{layerID}.attention.dense.weight'] att_dense_weight[:,:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.attention.projection.weight.npy').transpose() ).float() att_dense_bias = transformer[f'layers.{layerID}.attention.dense.bias'] att_dense_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.attention.projection.bias.npy') ).float() mlp_dense_h_to_4h_weight = transformer[f'layers.{layerID}.mlp.dense_h_to_4h.weight'] mlp_dense_h_to_4h_weight[:,:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.output.mapping.weight.npy').transpose() ).float() mlp_dense_h_to_4h_bias = transformer[f'layers.{layerID}.mlp.dense_h_to_4h.bias'] mlp_dense_h_to_4h_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.output.mapping.bias.npy') ).float() mlp_dense_4h_to_h_weight = transformer[f'layers.{layerID}.mlp.dense_4h_to_h.weight'] mlp_dense_4h_to_h_weight[:,:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.output.projection.weight.npy').transpose() ).float() mlp_dense_4h_to_h_bias = transformer[f'layers.{layerID}.mlp.dense_4h_to_h.bias'] mlp_dense_4h_to_h_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.output.projection.bias.npy') ).float() input_layernorm_weight = transformer[f'layers.{layerID}.input_layernorm.weight'] input_layernorm_weight[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.layernorm1.gamma.npy') ).float() input_layernorm_bias = transformer[f'layers.{layerID}.input_layernorm.bias'] input_layernorm_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.layernorm1.beta.npy') ).float() post_attention_layernorm_weight = transformer[f'layers.{layerID}.post_attention_layernorm.weight'] post_attention_layernorm_weight[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.layernorm2.gamma.npy') ).float() post_attention_layernorm_bias = transformer[f'layers.{layerID}.post_attention_layernorm.bias'] post_attention_layernorm_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.layernorm2.beta.npy') ).float() input_layernorm_weight = transformer[f'layers.{layerID}.input_layernorm.weight'] input_layernorm_weight[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.layernorm1.gamma.npy') ).float() input_layernorm_bias = transformer[f'layers.{layerID}.input_layernorm.bias'] input_layernorm_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.layernorm1.beta.npy') ).float() post_attention_layernorm_weight = transformer[f'layers.{layerID}.post_attention_layernorm.weight'] post_attention_layernorm_weight[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.layernorm2.gamma.npy') ).float() post_attention_layernorm_bias = transformer[f'layers.{layerID}.post_attention_layernorm.bias'] post_attention_layernorm_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.layernorm2.beta.npy') ).float() def loadQueryLayerFromNp(npCkptPath, transformer): attention_dense1_weight_np = \ np.load(npCkptPath+f'backbone.top_query_layer.attention.dense1.weight.npy') attention_dense1_bias_np = \ np.load(npCkptPath+f'backbone.top_query_layer.attention.dense1.bias.npy') attention_dense2_weight_np = \ np.load(npCkptPath+f'backbone.top_query_layer.attention.dense2.weight.npy') attention_dense2_bias_np = \ np.load(npCkptPath+f'backbone.top_query_layer.attention.dense2.bias.npy') attention_dense3_weight_np = \ np.load(npCkptPath+f'backbone.top_query_layer.attention.dense3.weight.npy') attention_dense3_bias_np = \ np.load(npCkptPath+f'backbone.top_query_layer.attention.dense3.bias.npy') query_weight = transformer[f'topQueryLayer.attention.query.weight'] query_weight[:, :] = \ torch.tensor(attention_dense1_weight_np).float() query_bias = transformer[f'topQueryLayer.attention.query.bias'] query_bias[:] = torch.tensor(attention_dense1_bias_np).float() key_weight = transformer[f'topQueryLayer.attention.key.weight'] key_weight[:, :] = \ torch.tensor(attention_dense2_weight_np).float() key_bias = transformer[f'topQueryLayer.attention.key.bias'] key_bias[:] = torch.tensor(attention_dense2_bias_np).float() value_weight = transformer[f'topQueryLayer.attention.value.weight'] value_weight[:, :] = \ torch.tensor(attention_dense3_weight_np).float() value_bias = transformer[f'topQueryLayer.attention.value.bias'] value_bias[:] = torch.tensor(attention_dense3_bias_np).float() att_dense_weight = transformer[f'topQueryLayer.attention.dense.weight'] att_dense_weight[:,:] = \ torch.tensor( np.load(npCkptPath+f'backbone.top_query_layer.attention.projection.weight.npy') .transpose() ).float() att_dense_bias = transformer[f'topQueryLayer.attention.dense.bias'] att_dense_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.top_query_layer.attention.projection.bias.npy') ).float() mlp_dense_h_to_4h_weight = transformer[f'topQueryLayer.mlp.dense_h_to_4h.weight'] mlp_dense_h_to_4h_weight[:,:] = \ torch.tensor( np.load(npCkptPath+f'backbone.top_query_layer.output.mapping.weight.npy') .transpose() ).float() mlp_dense_h_to_4h_bias = transformer[f'topQueryLayer.mlp.dense_h_to_4h.bias'] mlp_dense_h_to_4h_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.top_query_layer.output.mapping.bias.npy') ).float() mlp_dense_4h_to_h_weight = transformer[f'topQueryLayer.mlp.dense_4h_to_h.weight'] mlp_dense_4h_to_h_weight[:,:] = \ torch.tensor( np.load(npCkptPath+f'backbone.top_query_layer.output.projection.weight.npy') .transpose() ).float() mlp_dense_4h_to_h_bias = transformer[f'topQueryLayer.mlp.dense_4h_to_h.bias'] mlp_dense_4h_to_h_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.top_query_layer.output.projection.bias.npy') ).float() input_layernorm_weight = transformer[f'topQueryLayer.input_layernorm.weight'] input_layernorm_weight[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.top_query_layer.layernorm1.gamma.npy') ).float() input_layernorm_bias = transformer[f'topQueryLayer.input_layernorm.bias'] input_layernorm_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.top_query_layer.layernorm1.beta.npy') ).float() post_attention_layernorm_weight = transformer[f'topQueryLayer.post_attention_layernorm.weight'] post_attention_layernorm_weight[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.top_query_layer.layernorm2.gamma.npy') ).float() post_attention_layernorm_bias = transformer[f'topQueryLayer.post_attention_layernorm.bias'] post_attention_layernorm_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.top_query_layer.layernorm2.beta.npy') ).float() if __name__ == '__main__': main() pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/pretrain_bert.py000066400000000000000000000073751443434453700251670ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Pretrain BERT""" import torch import torch.nn.functional as F from megatron import get_args from megatron import print_rank_0 from megatron import get_timers from megatron import mpu from megatron.data.dataset_utils import build_train_valid_test_datasets from megatron.model import BertModel from megatron.training import pretrain from megatron.utils import reduce_losses def model_provider(): """Build the model.""" print_rank_0('building BERT model ...') model = BertModel( num_tokentypes=2, add_binary_head=True, parallel_output=True) return model def get_batch(data_iterator): """Build the batch.""" # Items and their type. keys = ['text', 'types', 'labels', 'is_random', 'loss_mask', 'padding_mask'] datatype = torch.int64 # Broadcast data. if data_iterator is not None: data = next(data_iterator) else: data = None data_b = mpu.broadcast_data(keys, data, datatype) # Unpack. tokens = data_b['text'].long() types = data_b['types'].long() sentence_order = data_b['is_random'].long() loss_mask = data_b['loss_mask'].float() lm_labels = data_b['labels'].long() padding_mask = data_b['padding_mask'].long() return tokens, types, sentence_order, loss_mask, lm_labels, padding_mask def forward_step(data_iterator, model): """Forward step.""" args = get_args() timers = get_timers() # Get the batch. timers('batch generator').start() tokens, types, sentence_order, loss_mask, lm_labels, padding_mask \ = get_batch(data_iterator) timers('batch generator').stop() # Forward model. lm_labels lm_loss_, sop_logits = model(tokens, padding_mask, tokentype_ids=types, lm_labels=lm_labels) sop_loss = F.cross_entropy(sop_logits.view(-1, 2).float(), sentence_order.view(-1), ignore_index=-1) lm_loss = torch.sum( lm_loss_.view(-1) * loss_mask.reshape(-1)) / loss_mask.sum() loss = lm_loss + sop_loss reduced_losses = reduce_losses([lm_loss, sop_loss]) return loss, {'lm loss': reduced_losses[0], 'sop loss': reduced_losses[1]} def train_valid_test_datasets_provider(train_val_test_num_samples): """Build train, valid, and test datasets.""" args = get_args() print_rank_0('> building train, validation, and test datasets ' 'for BERT ...') train_ds, valid_ds, test_ds = build_train_valid_test_datasets( data_prefix=args.data_path, data_impl=args.data_impl, splits_string=args.split, train_valid_test_num_samples=train_val_test_num_samples, max_seq_length=args.seq_length, masked_lm_prob=args.mask_prob, short_seq_prob=args.short_seq_prob, seed=args.seed, skip_warmup=(not args.mmap_warmup)) print_rank_0("> finished creating BERT datasets ...") return train_ds, valid_ds, test_ds if __name__ == "__main__": pretrain(train_valid_test_datasets_provider, model_provider, forward_step, args_defaults={'tokenizer_type': 'BertWordPieceLowerCase'}) pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/pretrain_evolution.py000066400000000000000000000067601443434453700262540ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. 2022, PCL. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Pretrain AlphaEnhanced""" import torch from megatron import get_args from megatron import print_rank_0 from megatron import get_timers from megatron import get_tokenizer from megatron import mpu from megatron.data.gpt2_dataset import build_train_valid_test_datasets from megatron.model import EnhancedModel from megatron.training import pretrain from megatron.utils import get_ltor_masks_and_position_ids from megatron.utils import reduce_losses def model_provider(): """Build the model.""" print_rank_0('building AlphaEnhanced model ...') model = EnhancedModel(num_tokentypes=0, parallel_output=True) return model def get_batch(data_iterator): """Generate a batch""" args = get_args() tokenizer = get_tokenizer() # Items and their type. keys = ['text'] datatype = torch.int64 # Broadcast data. if data_iterator is not None: data = next(data_iterator) else: data = None data_b = mpu.broadcast_data(keys, data, datatype) # Unpack. tokens_ = data_b['text'].long() labels = tokens_[:, 1:].contiguous() tokens = tokens_[:, :-1].contiguous() # Get the masks and postition ids. attention_mask, loss_mask, position_ids = get_ltor_masks_and_position_ids( tokens, tokenizer.eod, args.reset_position_ids, args.reset_attention_mask, args.eod_mask_loss) return tokens, labels, loss_mask, attention_mask, position_ids def forward_step(data_iterator, model): """Forward step.""" args = get_args() timers = get_timers() # Get the batch. timers('batch generator').start() tokens, labels, loss_mask, attention_mask, position_ids = get_batch(data_iterator) timers('batch generator').stop() # Forward model. losses, _ = model(tokens, position_ids, attention_mask, labels=labels) loss_mask = loss_mask.view(-1) loss = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum() # Reduce loss for logging. reduced_loss = reduce_losses([loss]) return loss, {'lm loss': reduced_loss[0]} def train_valid_test_datasets_provider(train_val_test_num_samples): """Build train, valid, and test datasets.""" args = get_args() print_rank_0('> building train, validation, and test datasets for AlphaEnhanced ...') train_ds, valid_ds, test_ds = build_train_valid_test_datasets( data_prefix=args.data_path, data_impl=args.data_impl, splits_string=args.split, train_valid_test_num_samples=train_val_test_num_samples, seq_length=args.seq_length, seed=args.seed, skip_warmup=(not args.mmap_warmup)) print_rank_0("> finished creating AlphaEnhanced datasets ...") return train_ds, valid_ds, test_ds if __name__ == "__main__": pretrain(train_valid_test_datasets_provider, model_provider, forward_step, args_defaults={'tokenizer_type': 'GPT2BPETokenizer'}) pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/pretrain_gpt2.py000066400000000000000000000066671443434453700251120ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Pretrain GPT2""" import torch from megatron import get_args from megatron import print_rank_0 from megatron import get_timers from megatron import get_tokenizer from megatron import mpu from megatron.data.gpt2_dataset import build_train_valid_test_datasets from megatron.model import GPT2Model from megatron.training import pretrain from megatron.utils import get_ltor_masks_and_position_ids from megatron.utils import reduce_losses def model_provider(): """Build the model.""" print_rank_0('building GPT2 model ...') model = GPT2Model(num_tokentypes=0, parallel_output=True) return model def get_batch(data_iterator): """Generate a batch""" args = get_args() tokenizer = get_tokenizer() # Items and their type. keys = ['text'] datatype = torch.int64 # Broadcast data. if data_iterator is not None: data = next(data_iterator) else: data = None data_b = mpu.broadcast_data(keys, data, datatype) # Unpack. tokens_ = data_b['text'].long() labels = tokens_[:, 1:].contiguous() tokens = tokens_[:, :-1].contiguous() # Get the masks and postition ids. attention_mask, loss_mask, position_ids = get_ltor_masks_and_position_ids( tokens, tokenizer.eod, args.reset_position_ids, args.reset_attention_mask, args.eod_mask_loss) return tokens, labels, loss_mask, attention_mask, position_ids def forward_step(data_iterator, model): """Forward step.""" args = get_args() timers = get_timers() # Get the batch. timers('batch generator').start() tokens, labels, loss_mask, attention_mask, position_ids = get_batch(data_iterator) timers('batch generator').stop() # Forward model. losses = model(tokens, position_ids, attention_mask, labels=labels) loss_mask = loss_mask.view(-1) loss = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum() # Reduce loss for logging. reduced_loss = reduce_losses([loss]) return loss, {'lm loss': reduced_loss[0]} def train_valid_test_datasets_provider(train_val_test_num_samples): """Build train, valid, and test datasets.""" args = get_args() print_rank_0('> building train, validation, and test datasets for GPT2 ...') train_ds, valid_ds, test_ds = build_train_valid_test_datasets( data_prefix=args.data_path, data_impl=args.data_impl, splits_string=args.split, train_valid_test_num_samples=train_val_test_num_samples, seq_length=args.seq_length, seed=args.seed, skip_warmup=(not args.mmap_warmup)) print_rank_0("> finished creating GPT2 datasets ...") return train_ds, valid_ds, test_ds if __name__ == "__main__": pretrain(train_valid_test_datasets_provider, model_provider, forward_step, args_defaults={'tokenizer_type': 'GPT2BPETokenizer'}) pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/pretrain_ict.py000066400000000000000000000120121443434453700247720ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Pretrain BERT for Inverse Cloze Task""" import torch import torch.distributed as dist import torch.nn.functional as F from megatron import get_args from megatron import print_rank_0 from megatron import get_timers from megatron import mpu from megatron.data.dataset_utils import build_train_valid_test_datasets from megatron.training import pretrain from megatron.utils import reduce_losses from megatron.model.realm_model import general_ict_model_provider from megatron.data.realm_dataset_utils import get_ict_batch def pretrain_ict_model_provider(): return general_ict_model_provider(False, False) def get_group_world_size_rank(): group = mpu.get_data_parallel_group() rank = torch.distributed.get_rank(group=group) world_size = torch.distributed.get_world_size(group=group) return group, rank, world_size class AllgatherFromDataParallelRegion(torch.autograd.Function): @staticmethod def forward(ctx, input_): assert input_.dim() == 2 group, rank, world_size = get_group_world_size_rank() tensor_list = [torch.empty_like(input_) for _ in range(world_size)] tensor_list[rank] = input_ torch.distributed.all_gather(tensor_list, input_, group=group) output = torch.cat(tensor_list, dim=0).contiguous() return output @staticmethod def backward(ctx, grad_output): group, rank, world_size = get_group_world_size_rank() assert grad_output.shape[0] % world_size == 0 dim_size = grad_output.shape[0] // world_size output_list = torch.split(grad_output, dim_size, dim=0) # get chunk from this rank output = output_list[rank].contiguous() return output def forward_step(data_iterator, model): """Forward step.""" args = get_args() timers = get_timers() # Get the batch. timers('batch generator').start() query_tokens, query_pad_mask, \ block_tokens, block_pad_mask, block_indices = get_ict_batch(data_iterator) timers('batch generator').stop() # Forward model. query_logits, block_logits = model(query_tokens, query_pad_mask, block_tokens, block_pad_mask) local_batch_size = query_logits.shape[0] global_batch_size = dist.get_world_size() * local_batch_size # recall we assert that model_parallel_size == 1 all_query_logits = AllgatherFromDataParallelRegion.apply(query_logits) all_block_logits = AllgatherFromDataParallelRegion.apply(block_logits) # scores are inner products between query and block embeddings retrieval_scores = all_query_logits.float().matmul(torch.transpose(all_block_logits, 0, 1).float()) softmaxed = F.softmax(retrieval_scores, dim=1) sorted_vals, sorted_indices = torch.topk(softmaxed, k=softmaxed.shape[1], sorted=True) def topk_accuracy(k): return torch.cuda.FloatTensor([sum([int(i in sorted_indices[i, :k]) for i in range(global_batch_size)]) / global_batch_size]) topk_accs = [topk_accuracy(int(k)) for k in args.report_topk_accuracies] retrieval_loss = torch.nn.CrossEntropyLoss()(retrieval_scores, torch.arange(global_batch_size).long().cuda()) reduced_losses = reduce_losses([retrieval_loss, *topk_accs]) # create stats_dict with retrieval loss and all specified top-k accuracies topk_acc_dict = {'top{}_acc'.format(k): v for k, v in zip(args.report_topk_accuracies, reduced_losses[1:])} stats_dict = dict(retrieval_loss=reduced_losses[0], **topk_acc_dict) return retrieval_loss, stats_dict def train_valid_test_datasets_provider(train_val_test_num_samples): """Build train, valid and test datasets.""" args = get_args() print_rank_0('> building train, validation, and test datasets ' 'for BERT ICT...') train_ds, valid_ds, test_ds = build_train_valid_test_datasets( data_prefix=args.data_path, data_impl=args.data_impl, splits_string=args.split, train_valid_test_num_samples=train_val_test_num_samples, max_seq_length=args.seq_length, masked_lm_prob=args.mask_prob, short_seq_prob=args.short_seq_prob, seed=args.seed, skip_warmup=(not args.mmap_warmup), dataset_type='ict') print_rank_0("> finished creating BERT ICT datasets ...") return train_ds, valid_ds, test_ds if __name__ == "__main__": pretrain(train_valid_test_datasets_provider, pretrain_ict_model_provider, forward_step, args_defaults={'tokenizer_type': 'BertWordPieceLowerCase'}) pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/requirements.txt000066400000000000000000000000371443434453700252250ustar00rootroot00000000000000pybind11 torch six regex numpy pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/setup.py000066400000000000000000000057071443434453700234640ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Setup for pip package.""" import os import sys import setuptools if sys.version_info < (3,): raise Exception("Python 2 is not supported by Megatron.") from megatron.package_info import ( __description__, __contact_names__, __url__, __download_url__, __keywords__, __license__, __package_name__, __version__, ) with open("README_mgt.md", "r") as fh: long_description = fh.read() ############################################################################### # Dependency Loading # # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% # def req_file(filename): with open(filename) as f: content = f.readlines() return [x.strip() for x in content] install_requires = req_file("requirements.txt") setuptools.setup( name=__package_name__, # Versions should comply with PEP440. For a discussion on single-sourcing # the version across setup.py and the project code, see # https://packaging.python.org/en/latest/single_source_version.html version=__version__, description=__description__, long_description=long_description, long_description_content_type="text/markdown", # The project's main homepage. url=__url__, author=__contact_names__, maintainer=__contact_names__, # The licence under which the project is released license=__license__, classifiers=[ 'Intended Audience :: Developers', 'Intended Audience :: Science/Research', 'Intended Audience :: Information Technology', # Indicate what your project relates to 'Topic :: Scientific/Engineering :: Artificial Intelligence', 'Topic :: Software Development :: Libraries :: Python Modules', # Supported python versions 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', # Additional Setting 'Environment :: Console', 'Natural Language :: English', 'Operating System :: OS Independent', ], python_requires='>=3.6', packages=setuptools.find_packages(), install_requires=install_requires, # Add in any packaged data. include_package_data=True, zip_safe=False, # PyPI package information. keywords=__keywords__ ) pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tasks/000077500000000000000000000000001443434453700230665ustar00rootroot00000000000000pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tasks/data_utils.py000066400000000000000000000067041443434453700256000ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Tasks data utility.""" import re import numpy as np def clean_text(text): """Remove new lines and multiple spaces and adjust end of sentence dot.""" text = text.replace("\n", " ") text = re.sub(r'\s+', ' ', text) for _ in range(3): text = text.replace(' . ', '. ') return text def build_sample(ids, types, paddings, label, unique_id): """Convert to numpy and return a sample consumed by the batch producer.""" ids_np = np.array(ids, dtype=np.int64) types_np = np.array(types, dtype=np.int64) paddings_np = np.array(paddings, dtype=np.int64) sample = ({'text': ids_np, 'types': types_np, 'padding_mask': paddings_np, 'label': int(label), 'uid': int(unique_id)}) return sample def build_tokens_types_paddings_from_text(text_a, text_b, tokenizer, max_seq_length): """Build token types and paddings, trim if needed, and pad if needed.""" text_a_ids = tokenizer.tokenize(text_a) text_b_ids = None if text_b is not None: text_b_ids = tokenizer.tokenize(text_b) return build_tokens_types_paddings_from_ids(text_a_ids, text_b_ids, max_seq_length, tokenizer.cls, tokenizer.sep, tokenizer.pad) def build_tokens_types_paddings_from_ids(text_a_ids, text_b_ids, max_seq_length, cls_id, sep_id, pad_id): """Build token types and paddings, trim if needed, and pad if needed.""" ids = [] types = [] paddings = [] # [CLS]. ids.append(cls_id) types.append(0) paddings.append(1) # A. len_text_a = len(text_a_ids) ids.extend(text_a_ids) types.extend([0] * len_text_a) paddings.extend([1] * len_text_a) # [SEP]. ids.append(sep_id) types.append(0) paddings.append(1) # B. if text_b_ids is not None: len_text_b = len(text_b_ids) ids.extend(text_b_ids) types.extend([1] * len_text_b) paddings.extend([1] * len_text_b) # Cap the size. trimmed = False if len(ids) >= max_seq_length: max_seq_length_m1 = max_seq_length - 1 ids = ids[0:max_seq_length_m1] types = types[0:max_seq_length_m1] paddings = paddings[0:max_seq_length_m1] trimmed = True # [SEP]. if (text_b_ids is not None) or trimmed: ids.append(sep_id) if text_b_ids is None: types.append(0) else: types.append(1) paddings.append(1) # Padding. padding_length = max_seq_length - len(ids) if padding_length > 0: ids.extend([pad_id] * padding_length) types.extend([pad_id] * padding_length) paddings.extend([0] * padding_length) return ids, types, paddings pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tasks/downstream_pangu/000077500000000000000000000000001443434453700264435ustar00rootroot00000000000000pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tasks/downstream_pangu/README.md000066400000000000000000000007251443434453700277260ustar00rootroot00000000000000#iflytek ```bash {'task': 'few_shot', 'max_len': 100, 'tag_new_example': True, 'few_shot_num_sample': 3, 'np_seed': 233}iflytek-few_shot: acc=1969/2501=0.78929 {'task': 'few_shot', 'max_len': 150, 'tag_new_example': True, 'few_shot_num_sample': 3, 'np_seed': 233} iflytek-few_shot: acc=2006/2599=0.77184 {'task': 'few_shot', 'max_len': 50, 'tag_new_example': True, 'few_shot_num_sample': 3, 'np_seed': 233,} iflytek-few_shot: acc=2002/2599=0.77030 time=2012.15 ```pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tasks/downstream_pangu/evaluate_classfier.py000066400000000000000000000107361443434453700326650ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Sample Generate GPT2""" import os import sys import numpy as np import torch import time import itertools sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir))) from megatron.text_generation_utils import pad_batch, get_batch from megatron import get_args from megatron import print_rank_0 from megatron import get_tokenizer from megatron.checkpointing import load_checkpoint from megatron.initialize import initialize_megatron from megatron.model import GPT2Model from megatron.training import get_model from megatron.text_generation_utils import generate_and_write_samples_unconditional from megatron.text_generation_utils import generate_samples_input_from_file from megatron.text_generation_utils import generate_samples_interactive # from megatron.model.transformer import LayerNorm from megatron import mpu from load_iflytek import iflytek_dataset def model_provider(): """Build the model.""" print_rank_0('building GPT2 model ...') model = GPT2Model(num_tokentypes=0, parallel_output=False) return model def add_text_generate_args(parser): """Text generation arguments.""" group = parser.add_argument_group(title='text generation') return parser def main(): """Main program.""" initialize_megatron(extra_args_provider=add_text_generate_args, args_defaults={'tokenizer_type': 'GPT2BPETokenizer'}) # Set up model and load checkpoint. model = get_model(model_provider) model.eval() args = get_args() if args.load is not None: _ = load_checkpoint(model, None, None) task = 'few_shot' config = [ ('task', [task]), #'zero_shot','one_shot','few_shot' ('max_len', [50]), #None,200,100 ('tag_new_example', [True]), #True, False ('few_shot_num_sample', [3]), #2,3,4 ('np_seed', [233]), #233,235,237,239 ('new_mask', [False]), #True, False ('input_str_format', [ # "{label}：{sentence}", "这是关于{label}的应用程序：{sentence}", ]) ] para_config_list = [{y0[0]:y1 for y0,y1 in zip(config,x)} for x in itertools.product(*[x[1] for x in config])] para_config = para_config_list[0] dataset = iflytek_dataset() samples = dataset.getSamples(para_config) acc = [] time_recorder = time.time() for ind,sample in enumerate(samples): loss_ = [] for class_sample,loss_mask in zip(sample['input_ids_list'],sample['loss_mask_list']): context_tokens_tensor = torch.cuda.LongTensor([class_sample]) labels = context_tokens_tensor[:, 1:].contiguous().cuda() tokens = context_tokens_tensor[:, :-1].contiguous() tokens, attention_mask, position_ids = get_batch(tokens) type_ids = None with torch.no_grad(): logits = model(tokens, position_ids, attention_mask, tokentype_ids=type_ids, forward_method_parallel_output=False) losses = mpu.vocab_parallel_cross_entropy( logits.contiguous().float(), labels.contiguous()) loss_mask = torch.cuda.LongTensor(loss_mask) loss = torch.sum( losses.view(-1) * loss_mask.contiguous().view(-1).float()) loss_.append(loss) predict = np.argmin(loss_) acc.append(predict==sample['label']) if (ind%100 == 0): time_cost = time.time() - time_recorder print(f'[{ind}] iflytek-{task}: acc={sum(acc)}/{len(acc)}={(sum(acc)/len(acc)):.5f} time={time_cost:.2f}') accuracy = sum(acc)/len(acc) print(f'{para_config} iflytek-{task}: acc={sum(acc)}/{len(acc)}={(sum(acc)/len(acc)):.5f} time={time_cost:.2f}') pass if __name__ == "__main__": main() pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tasks/downstream_pangu/load_iflytek.py000066400000000000000000000151761443434453700314750ustar00rootroot00000000000000 import os import itertools import numpy as np import json from megatron import get_tokenizer from megatron.tokenizer import JIEBATokenizer from megatron.utils import get_ltor_masks_and_position_ids class iflytek_dataset(object): def __init__(self): self.zc_cache = [] self.data_path = '/userhome/dataset/cluedatasets/iflytek_public/' vocab_file = '/userhome/pclproject/gpt/Megatron-LM-1.1-Pangu/megatron/tokenizer/bpe_4w_pcl/vocab' self.tokenizer = get_tokenizer() # self.tokenizer = JIEBATokenizer(vocab_file) self.seq_length = 1024 self.num_samples = 0 print() def load_iflytek_train_example_for_shot(self, data_path, num_sample=2, np_rng=None, max_len=None, input_str_format=None): if input_str_format is None: input_str_format = "这是关于{label}的应用程序：{sentence}" # input_str_format = "{s}：{label}" if np_rng is None: np_rng = np.random.default_rng() if len(self.zc_cache)>0: z0 = self.zc_cache[0] else: tmp0 = [os.path.join(data_path,x) for x in os.listdir(data_path) if x.startswith('train')] assert len(tmp0)==1 train_file = tmp0[0] with open(train_file, 'r', encoding='utf-8') as fid: z0 = [json.loads(x) for x in fid.readlines()] self.zc_cache.append(z0) # select sample with balanced labels hf0 = lambda x: x[1] tmp0 = itertools.groupby(sorted([(x,y['label_des']) for x,y in enumerate(z0)],key=hf0), key=hf0) group_index = [np.array([z[0] for z in y]) for x,y in tmp0] for x in group_index: np_rng.shuffle(x) #in-place tmp0 = (num_sample-1)//len(group_index) + 1 tmp1 = np.concatenate([x[:tmp0] for x in group_index]) np_rng.shuffle(tmp1) selected_index = tmp1[:num_sample] # selected_index = np_rng.permutation(len(z0))[:num_sample] examples = [] for x in selected_index: sentence = z0[x]['sentence'] if max_len is None else z0[x]['sentence'][:max_len] tmp0 = input_str_format.format(label=z0[x]['label_des'], sentence=sentence) examples.append(tmp0) ret = { 'zero_shot': '', 'one_shot': examples[0]+'\n', 'few_shot':('\n'.join(examples)) + '\n', } return ret def getSamples(self, para_config): with open(os.path.join(self.data_path, 'train.json'), "r", encoding="utf-8") as fid: ground_truth = [json.loads(x) for x in fid] id_to_label = {int(x['label']):x['label_des'] for x in ground_truth} task = para_config['task'] max_len = para_config['max_len'] tag_new_example = para_config['tag_new_example'] few_shot_num_sample = para_config['few_shot_num_sample'] np_seed = para_config['np_seed'] new_mask = para_config['new_mask'] input_str_format = para_config['input_str_format'] input_str_format_mask = input_str_format.rsplit('{',1)[0] input_str_format_mask_tag_label = '{label}' in input_str_format_mask np_seed = para_config['np_seed'] np_rng = np.random.default_rng(seed=np_seed) #must be same across model-parallel with open(os.path.join(self.data_path, 'dev.json'), "r", encoding="utf-8") as fid: tmp0 = [json.loads(x) for x in fid] #[:200] ground_truth = [tmp0[x] for x in np_rng.permutation(len(tmp0))] print('total num samples of iflytek dev dataset is : ',len(ground_truth)) z0 = [] zc_print_ind = 0 if not tag_new_example: shot_to_example = self.load_iflytek_train_example_for_shot(self.data_path, num_sample=few_shot_num_sample, np_rng=np_rng, max_len=max_len, input_str_format=input_str_format) example = shot_to_example[task] for instance in ground_truth: zc_print_ind += 1 if tag_new_example: shot_to_example = self.load_iflytek_train_example_for_shot(self.data_path, num_sample=few_shot_num_sample, np_rng=np_rng, max_len=max_len, input_str_format=input_str_format) example = shot_to_example[task] true_label = instance['label_des'] tmp0 = sorted(list(set(id_to_label.values()) - {true_label})) fake_label = [tmp0[x] for x in np_rng.permutation(len(tmp0))[:3]] #[:119] instance_tf_label = [true_label] + fake_label instance_tf_label = [instance_tf_label[x] for x in np_rng.permutation(len(instance_tf_label))] #shuffle input_ids_list = [] loss_mask_list = [] label_list = [] input_str_list = [] for label_i in instance_tf_label: if input_str_format_mask_tag_label: tmp0 = example + input_str_format_mask.format(label=label_i) else: tmp0 = example + input_str_format_mask.format(sentence=instance['sentence']) tmp0 = self.tokenizer.tokenize(tmp0) tmp1 = example + input_str_format.format(label=label_i, sentence=instance['sentence']) input_ids = self.tokenizer.tokenize(tmp1)[:self.seq_length] input_str_list.append(tmp1) # tmp0 = tokenizer.tokenize(f"{example}{instance['sentence']}") # input_ids = tokenizer.tokenize(f"{example}{instance['sentence']}：{label_i}")[:config.seq_length] mask = np.zeros(self.seq_length) mask[len(tmp0):len(input_ids)] = 1 # mask[:len(input_ids)] = 1 input_ids = np.pad(input_ids, ((0,self.seq_length+1-len(input_ids)),), 'constant', constant_values=(0,self.tokenizer.pad_id)) input_ids_list.append(input_ids) loss_mask_list.append(mask) label_list.append(label_i) tmp0 = input_ids_list tmp1 = loss_mask_list label = [x for x,y in enumerate(label_list) if y==true_label][0] yield {'input_ids_list':tmp0, "loss_mask_list":tmp1, 'label':label } # yield {'input_ids_list':tmp0, # "loss_mask_list":tmp1, # 'label':label, # 'input_str_list':input_str_list # } # z0.append((tmp0,tmp1,label,input_str_list)) if __name__ == '__main__': iflytek = iflytek_dataset() samples = iflytek.getSamples() for sample in samples: print() passpcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tasks/ensemble_classifier.py000066400000000000000000000130701443434453700274370ustar00rootroot00000000000000import os import argparse import collections import numpy as np import torch def process_files(args): all_predictions = collections.OrderedDict() all_labels = collections.OrderedDict() all_uid = collections.OrderedDict() for path in args.paths: path = os.path.join(path, args.prediction_name) try: data = torch.load(path) for dataset in data: name, d = dataset predictions, labels, uid = d if name not in all_predictions: all_predictions[name] = np.array(predictions) if args.labels is None: args.labels = [i for i in range(all_predictions[name].shape[1])] if args.eval: all_labels[name] = np.array(labels) all_uid[name] = np.array(uid) else: all_predictions[name] += np.array(predictions) assert np.allclose(all_uid[name], np.array(uid)) except Exception as e: print(e) continue return all_predictions, all_labels, all_uid def get_threshold(all_predictions, all_labels, one_threshold=False): if one_threshold: all_predictons = {'combined': np.concatenate(list(all_predictions.values()))} all_labels = {'combined': np.concatenate(list(all_predictions.labels()))} out_thresh = [] for dataset in all_predictions: preds = all_predictions[dataset] labels = all_labels[dataset] out_thresh.append(calc_threshold(preds, labels)) return out_thresh def calc_threshold(p, l): trials = [(i) * (1. / 100.) for i in range(100)] best_acc = float('-inf') best_thresh = 0 for t in trials: acc = ((apply_threshold(p, t).argmax(-1) == l).astype(float)).mean() if acc > best_acc: best_acc = acc best_thresh = t return best_thresh def apply_threshold(preds, t): assert (np.allclose(preds.sum(-1), np.ones(preds.shape[0]))) prob = preds[:, -1] thresholded = (prob >= t).astype(int) preds = np.zeros_like(preds) preds[np.arange(len(thresholded)), thresholded.reshape(-1)] = 1 return preds def threshold_predictions(all_predictions, threshold): if len(threshold) != len(all_predictions): threshold = [threshold[-1]] * (len(all_predictions) - len(threshold)) for i, dataset in enumerate(all_predictions): thresh = threshold[i] preds = all_predictions[dataset] all_predictions[dataset] = apply_threshold(preds, thresh) return all_predictions def postprocess_predictions(all_predictions, all_labels, args): for d in all_predictions: all_predictions[d] = all_predictions[d] / len(args.paths) if args.calc_threshold: args.threshold = get_threshold(all_predictions, all_labels, args.one_threshold) print('threshold', args.threshold) if args.threshold is not None: all_predictions = threshold_predictions(all_predictions, args.threshold) return all_predictions, all_labels def write_predictions(all_predictions, all_labels, all_uid, args): all_correct = 0 count = 0 for dataset in all_predictions: preds = all_predictions[dataset] preds = np.argmax(preds, -1) if args.eval: correct = (preds == all_labels[dataset]).sum() num = len(all_labels[dataset]) accuracy = correct / num count += num all_correct += correct accuracy = (preds == all_labels[dataset]).mean() print(accuracy) if not os.path.exists(os.path.join(args.outdir, dataset)): os.makedirs(os.path.join(args.outdir, dataset)) outpath = os.path.join( args.outdir, dataset, os.path.splitext( args.prediction_name)[0] + '.tsv') with open(outpath, 'w') as f: f.write('id\tlabel\n') f.write('\n'.join(str(uid) + '\t' + str(args.labels[p]) for uid, p in zip(all_uid[dataset], preds.tolist()))) if args.eval: print(all_correct / count) def ensemble_predictions(args): all_predictions, all_labels, all_uid = process_files(args) all_predictions, all_labels = postprocess_predictions(all_predictions, all_labels, args) write_predictions(all_predictions, all_labels, all_uid, args) def main(): parser = argparse.ArgumentParser() parser.add_argument('--paths', required=True, nargs='+', help='paths to checkpoint directories used in ensemble') parser.add_argument('--eval', action='store_true', help='compute accuracy metrics against labels (dev set)') parser.add_argument('--outdir', help='directory to place ensembled predictions in') parser.add_argument('--prediction-name', default='test_predictions.pt', help='name of predictions in checkpoint directories') parser.add_argument('--calc-threshold', action='store_true', help='calculate threshold classification') parser.add_argument('--one-threshold', action='store_true', help='use on threshold for all subdatasets') parser.add_argument('--threshold', nargs='+', default=None, type=float, help='user supplied threshold for classification') parser.add_argument('--labels', nargs='+', default=None, help='whitespace separated list of label names') args = parser.parse_args() ensemble_predictions(args) if __name__ == '__main__': main() pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tasks/eval_utils.py000066400000000000000000000114511443434453700256110ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Evaluation utilities.""" import os import time import torch from megatron import get_args from megatron import print_rank_0 from megatron import mpu from tasks.finetune_utils import build_data_loader from tasks.finetune_utils import process_batch def accuracy_func_provider(single_dataset_provider): """Provide function that calculates accuracies.""" args = get_args() # Build dataloaders. datapaths = args.valid_data dataloaders = [] for datapath in datapaths: dataset = single_dataset_provider(datapath) dataloader = build_data_loader( dataset, args.batch_size, num_workers=args.num_workers, drop_last=(mpu.get_data_parallel_world_size() > 1)) dataloaders.append((dataset.dataset_name, dataloader)) def metrics_func(model, epoch, output_predictions=False): print_rank_0('calculating metrics ...') correct = 0 total = 0 if output_predictions: assert mpu.get_data_parallel_world_size() == 1 named_predictions = [] names = 'predictions' for name, dataloader in dataloaders: output = calculate_correct_answers(name, model, dataloader, epoch, output_predictions) if not output_predictions: correct_ans, total_count = output else: correct_ans, total_count, predictions = output named_predictions.append((name, predictions)) names += '_' + name correct += correct_ans total += total_count percent = float(correct) * 100.0 / float(total) print_rank_0(' >> |epoch: {}| overall: correct / total = {} / {} = ' '{:.4f} %'.format(epoch, correct, total, percent)) if output_predictions and torch.distributed.get_rank() == 0: assert args.load is not None filename = os.path.join(args.load, names + '.pt') torch.save(named_predictions, filename) return metrics_func def calculate_correct_answers(name, model, dataloader, epoch, output_predictions): """Calculate correct over total answers and return prediction if the `output_predictions` is true.""" start_time = time.time() model.eval() with torch.no_grad(): # For all the batches in the dataset. total = 0 correct = 0 if output_predictions: # This option is only possible when data parallel size is 1. assert mpu.get_data_parallel_world_size() == 1 softmaxes = [] labels = [] ids = [] for _, batch in enumerate(dataloader): # Run the model forward. tokens, types, labels_, attention_mask = process_batch(batch) logits = model(tokens, attention_mask, types) # Add output predictions. if output_predictions: softmaxes.extend(torch.nn.Softmax(dim=-1)( logits.float()).data.cpu().numpy().tolist()) labels.extend(labels_.data.cpu().numpy().tolist()) ids.extend(batch['uid'].cpu().numpy().tolist()) # Compute the correct answers. predicted = torch.argmax(logits, dim=-1) corrects = (predicted == labels_) # Add to the counters. total += labels_.size(0) correct += corrects.sum().item() model.train() # Reduce. unreduced = torch.cuda.LongTensor([correct, total]) torch.distributed.all_reduce(unreduced, group=mpu.get_data_parallel_group()) # Print on screen. correct_ans = unreduced[0].item() total_count = unreduced[1].item() percent = float(correct_ans) * 100.0 / float(total_count) elapsed_time = time.time() - start_time print_rank_0(' > |epoch: {}| metrics for {}: correct / total ' '= {} / {} = {:.4f} %, elapsed time (sec): {:.3f}'.format( epoch, name, correct_ans, total_count, percent, elapsed_time)) if output_predictions: return correct_ans, total_count, (softmaxes, labels, ids) return correct_ans, total_count pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tasks/finetune_utils.py000066400000000000000000000231621443434453700265010ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Finetune utilities.""" import torch from megatron import get_args from megatron import print_rank_0 from megatron import get_timers from megatron import mpu from megatron.checkpointing import load_checkpoint from megatron.checkpointing import save_checkpoint from megatron.training import evaluate_and_print_results from megatron.training import setup_model_and_optimizer from megatron.training import train_step from megatron.training import training_log from megatron.utils import check_adlr_autoresume_termination from megatron.utils import reduce_losses def process_batch(batch): """Process batch and produce inputs for the model.""" args = get_args() tokens = batch['text'].long().cuda().contiguous() types = batch['types'].long().cuda().contiguous() labels = batch['label'].long().cuda().contiguous() attention_mask = batch['padding_mask'].float().cuda().contiguous() if args.fp16: attention_mask = attention_mask.half() return tokens, types, labels, attention_mask def _cross_entropy_forward_step(batch, model): """Simple forward step with cross-entropy loss.""" timers = get_timers() # Get the batch. timers('batch generator').start() try: batch_ = next(batch) except BaseException: batch_ = batch tokens, types, labels, attention_mask = process_batch(batch_) timers('batch generator').stop() # Forward model. logits = model(tokens, attention_mask, types) # Cross-entropy loss. loss_func = torch.nn.CrossEntropyLoss() loss = loss_func(logits.contiguous().float(), labels) # Reduce loss for logging. reduced_loss = reduce_losses([loss]) return loss, {'lm loss': reduced_loss[0]} def build_data_loader(dataset, batch_size, num_workers, drop_last): """Data loader. Note that batch-size is the local (per GPU) batch-size.""" # Sampler. world_size = mpu.get_data_parallel_world_size() rank = mpu.get_data_parallel_rank() sampler = torch.utils.data.distributed.DistributedSampler( dataset, num_replicas=world_size, rank=rank) # Data loader. Note that batch size is the per GPU batch size. data_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, sampler=sampler, shuffle=False, num_workers=num_workers, drop_last=drop_last, pin_memory=True) return data_loader def _build_infinite_size_dataloader(dataloader): """Build a looped dataloader with infinite size.""" iterator = dataloader.__iter__() while True: try: yield iterator.__next__() except StopIteration: iterator = dataloader.__iter__() def _build_train_valid_dataloaders(train_dataset, valid_dataset): """Traing and validation dataloaders.""" args = get_args() print_rank_0('building train and validation dataloaders ...') # Training dataset. train_dataloader = build_data_loader(train_dataset, args.batch_size, args.num_workers, not args.keep_last) # Set the training iterations. args.train_iters_per_epoch = len(train_dataloader) args.train_iters = args.epochs * args.train_iters_per_epoch # Validation dataset. For this dataset, we do not need to set up # shuffling so we can just use a simple infinite loop. valid_dataloader_ = build_data_loader(valid_dataset, args.batch_size, args.num_workers, not args.keep_last) valid_dataloader = _build_infinite_size_dataloader(valid_dataloader_) return train_dataloader, valid_dataloader def _train(model, optimizer, lr_scheduler, forward_step, train_dataloader, valid_dataloader, end_of_epoch_callback): """Train the model.""" args = get_args() timers = get_timers() # Turn on training mode which enables dropout. model.train() # Tracking loss. losses_dict_sum = {} # Starting epoch and iteration start_epoch = args.iteration // args.train_iters_per_epoch start_iteration = args.iteration % args.train_iters_per_epoch iteration = args.iteration # Memory reporting flag. report_memory_flag = True # For each remaining epoch timers('interval time').start() for epoch in range(start_epoch, args.epochs): print_rank_0('working on epoch {} ...'.format(epoch + 1)) # Set the data loader epoch to shuffle the index iterator. train_dataloader.sampler.set_epoch(args.seed + epoch) # For all the batches in the dataset. for iteration_, batch in enumerate(train_dataloader): # Ignore the iterations before starting value if iteration_ < start_iteration: continue # Set to zero so the next epoch does not skip any batches. start_iteration = 0 # Train for one step. losses_dict, _ = train_step(forward_step, batch, model, optimizer, lr_scheduler) iteration += 1 # Logging. report_memory_flag = training_log(losses_dict, losses_dict_sum, optimizer.param_groups[0]['lr'], iteration, optimizer.loss_scale, report_memory_flag) # Autoresume if args.adlr_autoresume and \ (iteration % args.adlr_autoresume_interval == 0): check_adlr_autoresume_termination(iteration, model, optimizer, lr_scheduler) # Checkpointing if args.save and args.save_interval and \ iteration % args.save_interval == 0: save_checkpoint(iteration, model, optimizer, lr_scheduler) # Evaluation if args.eval_interval and iteration % args.eval_interval == 0: prefix = 'iteration {}'.format(iteration) evaluate_and_print_results(prefix, forward_step, valid_dataloader, model, iteration, False) # Checkpointing at the end of each epoch. if args.save: save_checkpoint(iteration, model, optimizer, lr_scheduler) # Callback at the end of each epoch. if end_of_epoch_callback is not None: end_of_epoch_callback(model, epoch) def finetune(train_valid_datasets_provider, model_provider, forward_step=_cross_entropy_forward_step, end_of_epoch_callback_provider=None): """Main finetune function used across all tasks.""" args = get_args() timers = get_timers() # Train and validation data loaders. timers('train/valid/test dataset/dataloder').start() if args.epochs > 0: train_dataset, valid_dataset = train_valid_datasets_provider() train_dataloader, valid_dataloader = _build_train_valid_dataloaders( train_dataset, valid_dataset) timers('train/valid/test dataset/dataloder').stop() # Build calback function. timers('callback function').start() end_of_epoch_callback = None if end_of_epoch_callback_provider is not None: end_of_epoch_callback = end_of_epoch_callback_provider() timers('callback function').stop() # Build model, optimizer and learning rate scheduler. timers('model and optimizer').start() model, optimizer, lr_scheduler = setup_model_and_optimizer(model_provider) timers('model and optimizer').stop() # If pretrained checkpoint is provided and we have not trained for # any iteration (i.e., iteration is zero), then load the pretrained # checkpoint. timers('pretrained checkpoint').start() if args.iteration == 0 and args.pretrained_checkpoint is not None: original_load = args.load args.load = args.pretrained_checkpoint _ = load_checkpoint(model, None, None) args.load = original_load # This is critical when only model is loaded. We should make sure # master parameters are also updated. if args.fp16: optimizer._model_params_to_master_params() timers('pretrained checkpoint').stop() # Print setup timing. print_rank_0('done with setups ...') timers.log(['train/valid/test dataset/dataloder', 'callback function', 'model and optimizer', 'pretrained checkpoint']) print_rank_0('training ...') # Finetune the model. if args.epochs > 0: _train(model, optimizer, lr_scheduler, forward_step, train_dataloader, valid_dataloader, end_of_epoch_callback) # Or just evaluate. else: if end_of_epoch_callback is not None: print_rank_0('evaluation only mode, setting epoch to -1') end_of_epoch_callback(model, epoch=-1, output_predictions=True) print_rank_0('done :-)') pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tasks/glue/000077500000000000000000000000001443434453700240225ustar00rootroot00000000000000pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tasks/glue/data.py000066400000000000000000000047741443434453700253210ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """GLUE dataset.""" from abc import ABC from abc import abstractmethod from torch.utils.data import Dataset from megatron import print_rank_0 from tasks.data_utils import build_sample from tasks.data_utils import build_tokens_types_paddings_from_text class GLUEAbstractDataset(ABC, Dataset): """GLUE base dataset class.""" def __init__(self, task_name, dataset_name, datapaths, tokenizer, max_seq_length): # Store inputs. self.task_name = task_name self.dataset_name = dataset_name self.tokenizer = tokenizer self.max_seq_length = max_seq_length print_rank_0(' > building {} dataset for {}:'.format(self.task_name, self.dataset_name)) # Process the files. string = ' > paths:' for path in datapaths: string += ' ' + path print_rank_0(string) self.samples = [] for datapath in datapaths: self.samples.extend(self.process_samples_from_single_path(datapath)) print_rank_0(' >> total number of samples: {}'.format( len(self.samples))) def __len__(self): return len(self.samples) def __getitem__(self, idx): raw_sample = self.samples[idx] ids, types, paddings = build_tokens_types_paddings_from_text( raw_sample['text_a'], raw_sample['text_b'], self.tokenizer, self.max_seq_length) sample = build_sample(ids, types, paddings, raw_sample['label'], raw_sample['uid']) return sample @abstractmethod def process_samples_from_single_path(self, datapath): """Abstract method that takes a single path / filename and returns a list of dataset samples, each sample being a dict of {'text_a': string, 'text_b': string, 'label': int, 'uid': int} """ pass pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tasks/glue/finetune.py000066400000000000000000000056571443434453700262260ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """GLUE finetuning/evaluation.""" from megatron import get_args from megatron import print_rank_0 from megatron import get_tokenizer from megatron.model.classification import Classification from tasks.eval_utils import accuracy_func_provider from tasks.finetune_utils import finetune def glue_classification(num_classes, Dataset, name_from_datapath_func): def train_valid_datasets_provider(): """Build train and validation dataset.""" args = get_args() tokenizer = get_tokenizer() train_dataset = Dataset('training', args.train_data, tokenizer, args.seq_length) valid_dataset = Dataset('validation', args.valid_data, tokenizer, args.seq_length) return train_dataset, valid_dataset def model_provider(): """Build the model.""" args = get_args() print_rank_0('building classification model for {} ...'.format( args.task)) return Classification(num_classes=num_classes, num_tokentypes=2) def metrics_func_provider(): """Privde metrics callback function.""" def single_dataset_provider(datapath): args = get_args() tokenizer = get_tokenizer() name = name_from_datapath_func(datapath) return Dataset(name, [datapath], tokenizer, args.seq_length) return accuracy_func_provider(single_dataset_provider) """Finetune/evaluate.""" finetune(train_valid_datasets_provider, model_provider, end_of_epoch_callback_provider=metrics_func_provider) def main(): args = get_args() if args.task == 'MNLI': num_classes = 3 from tasks.glue.mnli import MNLIDataset as Dataset def name_from_datapath(datapath): return datapath.split('MNLI')[-1].strip( '.tsv').strip('/').replace('_', '-') elif args.task == 'QQP': num_classes = 2 from tasks.glue.qqp import QQPDataset as Dataset def name_from_datapath(datapath): return datapath.split('QQP')[-1].strip( '.tsv').strip('/').replace('_', '-') else: raise NotImplementedError('GLUE task {} is not implemented.'.format( args.task)) glue_classification(num_classes, Dataset, name_from_datapath) pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tasks/glue/mnli.py000066400000000000000000000060701443434453700253360ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """MNLI dataset.""" from megatron import print_rank_0 from tasks.data_utils import clean_text from .data import GLUEAbstractDataset LABELS = {'contradiction': 0, 'entailment': 1, 'neutral': 2} class MNLIDataset(GLUEAbstractDataset): def __init__(self, name, datapaths, tokenizer, max_seq_length, test_label='contradiction'): self.test_label = test_label super().__init__('MNLI', name, datapaths, tokenizer, max_seq_length) def process_samples_from_single_path(self, filename): """"Implement abstract method.""" print_rank_0(' > Processing {} ...'.format(filename)) samples = [] total = 0 first = True is_test = False with open(filename, 'r') as f: for line in f: row = line.strip().split('\t') if first: first = False if len(row) == 10: is_test = True print_rank_0( ' reading {}, {} and {} columns and setting ' 'labels to {}'.format( row[0].strip(), row[8].strip(), row[9].strip(), self.test_label)) else: print_rank_0(' reading {} , {}, {}, and {} columns ' '...'.format( row[0].strip(), row[8].strip(), row[9].strip(), row[-1].strip())) continue text_a = clean_text(row[8].strip()) text_b = clean_text(row[9].strip()) unique_id = int(row[0].strip()) label = row[-1].strip() if is_test: label = self.test_label assert len(text_a) > 0 assert len(text_b) > 0 assert label in LABELS assert unique_id >= 0 sample = {'text_a': text_a, 'text_b': text_b, 'label': LABELS[label], 'uid': unique_id} total += 1 samples.append(sample) if total % 50000 == 0: print_rank_0(' > processed {} so far ...'.format(total)) print_rank_0(' >> processed {} samples.'.format(len(samples))) return samples pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tasks/glue/qqp.py000066400000000000000000000076731443434453700252120ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """QQP dataset.""" from megatron import print_rank_0 from tasks.data_utils import clean_text from .data import GLUEAbstractDataset LABELS = [0, 1] class QQPDataset(GLUEAbstractDataset): def __init__(self, name, datapaths, tokenizer, max_seq_length, test_label=0): self.test_label = test_label super().__init__('QQP', name, datapaths, tokenizer, max_seq_length) def process_samples_from_single_path(self, filename): """"Implement abstract method.""" print_rank_0(' > Processing {} ...'.format(filename)) samples = [] total = 0 first = True is_test = False with open(filename, 'r') as f: for line in f: row = line.strip().split('\t') if first: first = False if len(row) == 3: is_test = True print_rank_0(' reading {}, {}, and {} columns and ' 'setting labels to {}'.format( row[0].strip(), row[1].strip(), row[2].strip(), self.test_label)) else: assert len(row) == 6 print_rank_0(' reading {}, {}, {}, and {} columns' ' ...'.format( row[0].strip(), row[3].strip(), row[4].strip(), row[5].strip())) continue if is_test: assert len(row) == 3, 'expected length 3: {}'.format(row) uid = int(row[0].strip()) text_a = clean_text(row[1].strip()) text_b = clean_text(row[2].strip()) label = self.test_label assert len(text_a) > 0 assert len(text_b) > 0 else: if len(row) == 6: uid = int(row[0].strip()) text_a = clean_text(row[3].strip()) text_b = clean_text(row[4].strip()) label = int(row[5].strip()) else: print_rank_0('***WARNING*** index error, ' 'skipping: {}'.format(row)) continue if len(text_a) == 0: print_rank_0('***WARNING*** zero length a, ' 'skipping: {}'.format(row)) continue if len(text_b) == 0: print_rank_0('***WARNING*** zero length b, ' 'skipping: {}'.format(row)) continue assert label in LABELS assert uid >= 0 sample = {'uid': uid, 'text_a': text_a, 'text_b': text_b, 'label': label} total += 1 samples.append(sample) if total % 50000 == 0: print_rank_0(' > processed {} so far ...'.format(total)) print_rank_0(' >> processed {} samples.'.format(len(samples))) return samples pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tasks/main.py000066400000000000000000000052331443434453700243670ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Main tasks functionality.""" import os import sys sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir))) from megatron import get_args from megatron.initialize import initialize_megatron def get_tasks_args(parser): """Provide extra arguments required for tasks.""" group = parser.add_argument_group(title='tasks') group.add_argument('--task', type=str, required=True, help='Task name.') group.add_argument('--epochs', type=int, default=None, help='Number of finetunning epochs. Zero results in ' 'evaluation only.') group.add_argument('--pretrained-checkpoint', type=str, default=None, help='Pretrained checkpoint used for finetunning.') group.add_argument('--keep-last', action='store_true', help='Keep the last batch (maybe incomplete) in' 'the data loader') group.add_argument('--train-data', nargs='+', default=None, help='Whitespace separated paths or corpora names ' 'for training.') group.add_argument('--valid-data', nargs='*', default=None, help='path(s) to the validation data.') group.add_argument('--overlapping-eval', type=int, default=32, help='Sliding window for overlapping evaluation.') group.add_argument('--strict-lambada', action='store_true', help='Use more difficult formulation of lambada.') return parser if __name__ == '__main__': initialize_megatron(extra_args_provider=get_tasks_args) args = get_args() if args.task == 'RACE': from race.finetune import main elif args.task in ['MNLI', 'QQP']: from glue.finetune import main elif args.task in ['LAMBADA', 'WIKITEXT103']: from zeroshot_gpt2.evaluate import main else: raise NotImplementedError('Task {} is not implemented.'.format( args.task)) main() pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tasks/race/000077500000000000000000000000001443434453700240005ustar00rootroot00000000000000pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tasks/race/data.py000066400000000000000000000107311443434453700252650ustar00rootroot00000000000000 import glob import json import os import time from torch.utils.data import Dataset from megatron import print_rank_0 from tasks.data_utils import build_sample from tasks.data_utils import build_tokens_types_paddings_from_ids from tasks.data_utils import clean_text NUM_CHOICES = 4 MAX_QA_LENGTH = 128 class RaceDataset(Dataset): def __init__(self, dataset_name, datapaths, tokenizer, max_seq_length, max_qa_length=MAX_QA_LENGTH): self.dataset_name = dataset_name print_rank_0(' > building RACE dataset for {}:'.format( self.dataset_name)) string = ' > paths:' for path in datapaths: string += ' ' + path print_rank_0(string) self.samples = [] for datapath in datapaths: self.samples.extend(process_single_datapath(datapath, tokenizer, max_qa_length, max_seq_length)) print_rank_0(' >> total number of samples: {}'.format( len(self.samples))) def __len__(self): return len(self.samples) def __getitem__(self, idx): return self.samples[idx] def process_single_datapath(datapath, tokenizer, max_qa_length, max_seq_length): """Read in RACE files, combine, clean-up, tokenize, and convert to samples.""" print_rank_0(' > working on {}'.format(datapath)) start_time = time.time() # Get list of files. filenames = glob.glob(os.path.join(datapath, '*.txt')) samples = [] num_docs = 0 num_questions = 0 num_samples = 0 # Load all the files for filename in filenames: with open(filename, 'r') as f: for line in f: data = json.loads(line) num_docs += 1 context = data["article"] questions = data["questions"] choices = data["options"] answers = data["answers"] # Check the length. assert len(questions) == len(answers) assert len(questions) == len(choices) # Context: clean up and convert to ids. context = clean_text(context) context_ids = tokenizer.tokenize(context) # Loop over questions. for qi, question in enumerate(questions): num_questions += 1 # Label. label = ord(answers[qi]) - ord("A") assert label >= 0 assert label < NUM_CHOICES assert len(choices[qi]) == NUM_CHOICES # For each question, build num-choices samples. ids_list = [] types_list = [] paddings_list = [] for ci in range(NUM_CHOICES): choice = choices[qi][ci] # Merge with choice. if "_" in question: qa = question.replace("_", choice) else: qa = " ".join([question, choice]) # Clean QA. qa = clean_text(qa) # Tokenize. qa_ids = tokenizer.tokenize(qa) # Trim if needed. if len(qa_ids) > max_qa_length: qa_ids = qa_ids[0:max_qa_length] # Build the sample. ids, types, paddings \ = build_tokens_types_paddings_from_ids( qa_ids, context_ids, max_seq_length, tokenizer.cls, tokenizer.sep, tokenizer.pad) ids_list.append(ids) types_list.append(types) paddings_list.append(paddings) # Convert to numpy and add to samples samples.append(build_sample(ids_list, types_list, paddings_list, label, num_samples)) num_samples += 1 elapsed_time = time.time() - start_time print_rank_0(' > processed {} document, {} questions, and {} samples' ' in {:.2f} seconds'.format(num_docs, num_questions, num_samples, elapsed_time)) return samples pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tasks/race/finetune.py000066400000000000000000000037621443434453700261770ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Race.""" from megatron import get_args from megatron import print_rank_0 from megatron import get_tokenizer from megatron.model.multiple_choice import MultipleChoice from tasks.eval_utils import accuracy_func_provider from tasks.finetune_utils import finetune from tasks.race.data import RaceDataset def train_valid_datasets_provider(): """Provide train and validation datasets.""" args = get_args() tokenizer = get_tokenizer() train_dataset = RaceDataset('training', args.train_data, tokenizer, args.seq_length) valid_dataset = RaceDataset('validation', args.valid_data, tokenizer, args.seq_length) return train_dataset, valid_dataset def model_provider(): """Build the model.""" print_rank_0('building multichoice model for RACE ...') return MultipleChoice(num_tokentypes=2) def metrics_func_provider(): """Privde metrics callback function.""" args = get_args() tokenizer = get_tokenizer() def single_dataset_provider(datapath): name = datapath.split('RACE')[-1].strip('/').replace('/', '-') return RaceDataset(name, [datapath], tokenizer, args.seq_length) return accuracy_func_provider(single_dataset_provider) def main(): finetune(train_valid_datasets_provider, model_provider, end_of_epoch_callback_provider=metrics_func_provider) pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tasks/zeroshot_gpt2/000077500000000000000000000000001443434453700256775ustar00rootroot00000000000000pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tasks/zeroshot_gpt2/datasets.py000066400000000000000000000174111443434453700300650ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Zero-shot datasets.""" import json import math import numpy as np import torch from megatron import get_args from megatron import print_rank_0 from megatron import get_tokenizer from .detokenizer import get_detokenizer def build_dataset(task): """Helper function to select and build dataset.""" if task == 'LAMBADA': return _build_lambada_dataset() if task == 'WIKITEXT103': return _build_wikitext103_dataset() if task == 'iflytek': return _build_iflytek_dataset() raise NotImplementedError('dataset for {} task is not ' 'implemented.'.format(task)) class _LMDataset(torch.utils.data.Dataset): def __init__(self, tokens, seq_len, pad_idx, num_original_tokens, num_tokenized_tokens, overalapping_eval=None): self.tokens = tokens self.seq_len = seq_len self.pad_idx = pad_idx self.overalapping_eval = overalapping_eval if self.overalapping_eval is None: self.overalapping_eval = self.seq_len self.overalapping_eval = max(1, self.overalapping_eval) self.num_original_tokens = num_original_tokens self.num_tokenized_tokens = num_tokenized_tokens self.total_targets = len(self.tokens) - 1 # remove first sequence tokens targets = max(self.total_targets - self.overalapping_eval, 0) self.total_sequences = max( math.ceil(targets / self.overalapping_eval) + 1, 1) def __len__(self): return self.total_sequences def __getitem__(self, idx): start_idx = idx * self.overalapping_eval end_idx = start_idx + self.seq_len tokens = self.tokens[start_idx:end_idx + 1] num_tokens = len(tokens) pad_mask = [1] * num_tokens if num_tokens < self.seq_len + 1: num_pad = (self.seq_len + 1 - num_tokens) pad_mask += [0] * (num_pad) tokens += [self.pad_idx] * num_pad pad_mask = np.array(pad_mask[1:]) if self.overalapping_eval != self.seq_len and idx != 0: pad_mask[:-self.overalapping_eval] *= 0 return {'text': np.array(tokens), 'pad_mask': pad_mask} class _IflytekDataset(torch.utils.data.Dataset): def __init__(self, tokens, seq_len, pad_idx, num_original_tokens, num_tokenized_tokens, overalapping_eval=None): self.tokens = tokens self.seq_len = seq_len self.pad_idx = pad_idx self.overalapping_eval = overalapping_eval if self.overalapping_eval is None: self.overalapping_eval = self.seq_len self.overalapping_eval = max(1, self.overalapping_eval) self.num_original_tokens = num_original_tokens self.num_tokenized_tokens = num_tokenized_tokens self.total_targets = len(self.tokens) - 1 # remove first sequence tokens targets = max(self.total_targets - self.overalapping_eval, 0) self.total_sequences = max( math.ceil(targets / self.overalapping_eval) + 1, 1) def __len__(self): return self.total_sequences def __getitem__(self, idx): start_idx = idx * self.overalapping_eval end_idx = start_idx + self.seq_len tokens = self.tokens[start_idx:end_idx + 1] num_tokens = len(tokens) pad_mask = [1] * num_tokens if num_tokens < self.seq_len + 1: num_pad = (self.seq_len + 1 - num_tokens) pad_mask += [0] * (num_pad) tokens += [self.pad_idx] * num_pad pad_mask = np.array(pad_mask[1:]) if self.overalapping_eval != self.seq_len and idx != 0: pad_mask[:-self.overalapping_eval] *= 0 return {'text': np.array(tokens), 'pad_mask': pad_mask} class _LambadaDataset(torch.utils.data.Dataset): def __init__(self, path, pad_idx, tokenizer, seq_len, strict=False): print_rank_0('> building lambada dataset from {} ...'.format(path)) self.seq_len = seq_len self.pad_idx = pad_idx self.tokenizer = tokenizer self.strict = strict self.tokens = [] self.labels = [] with open(path, 'r') as f: for line in f.readlines(): text = json.loads(line)['text'] tokens, labels = self.get_tokens(text) self.tokens.append(tokens) self.labels.append(labels) def get_tokens(self, text): if not self.strict: tokens = self.tokenizer.tokenize(text) return tokens[:-1], [tokens[-1]] last_token = text.split()[-1] start_idx = text.rfind(last_token) beginning_tokens = self.tokenizer.tokenize(text[:start_idx].strip()) last_token = self.tokenizer.tokenize(' ' + last_token) return beginning_tokens, last_token def __len__(self): return len(self.tokens) def __getitem__(self, idx): tokens = self.tokens[idx] num_tokens = len(tokens) pad_mask = [0] * num_tokens labels = self.labels[idx] pad_mask += [1] * len(labels) tokens = tokens + labels num_tokens = len(tokens) if num_tokens < self.seq_len + 1: num_pad = (self.seq_len + 1 - num_tokens) pad_mask += [0] * (num_pad) tokens += [self.pad_idx] * num_pad pad_mask = np.array(pad_mask[1:]) return {'text': np.array(tokens), 'pad_mask': pad_mask} def _build_lambada_dataset(): """Build lambada dataset.""" args = get_args() tokenizer = get_tokenizer() assert len(args.valid_data) == 1 val_dataset = _LambadaDataset(args.valid_data[0], tokenizer.eod, tokenizer, args.seq_length, args.strict_lambada) print_rank_0(' > found {} samples.'.format(len(val_dataset))) return val_dataset def _build_wikitext103_dataset(): """""" args = get_args() tokenizer = get_tokenizer() assert len(args.valid_data) == 1 with open(args.valid_data[0], "rb") as reader: entire_data = reader.read().decode('utf-8') num_original_tokens = len(entire_data.strip().split(" ")) entire_data = get_detokenizer(args.valid_data[0])(entire_data) tokenized_data = tokenizer.tokenize(entire_data) num_tokenized_tokens = len(tokenized_data) val_dataset = _LMDataset(tokenized_data, args.seq_length, tokenizer.eod, num_original_tokens, num_tokenized_tokens, args.overlapping_eval) print_rank_0(' > number of original tokens: {}, number of detokenized ' 'tokens: {}'.format(num_original_tokens, num_tokenized_tokens)) return val_dataset def _build_iflytek_dataset(): """""" args = get_args() tokenizer = get_tokenizer() assert len(args.valid_data) == 1 tokenized_data = tokenizer.tokenize(entire_data) num_tokenized_tokens = len(tokenized_data) val_dataset = _LMDataset(tokenized_data, args.seq_length, tokenizer.eod, num_original_tokens, num_tokenized_tokens, args.overlapping_eval) print_rank_0(' > number of original tokens: {}, number of detokenized ' 'tokens: {}'.format(num_original_tokens, num_tokenized_tokens)) return val_dataset pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tasks/zeroshot_gpt2/detokenizer.py000066400000000000000000000050261443434453700305770ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Detokenization.""" import re def ptb_detokenizer(string): string = string.replace(" '", "'") string = string.replace(" \n", "\n") string = string.replace("\n ", "\n") string = string.replace(" n't", "n't") string = string.replace(" N ", "1 ") string = string.replace("$ 1", "$1") string = string.replace("# 1", "#1") return string def wikitext_detokenizer(string): # contractions string = string.replace("s '", "s'") string = re.sub(r"/' [0-9]/", r"/'[0-9]/", string) # number separators string = string.replace(" @-@ ", "-") string = string.replace(" @,@ ", ",") string = string.replace(" @.@ ", ".") # punctuation string = string.replace(" : ", ": ") string = string.replace(" ; ", "; ") string = string.replace(" . ", ". ") string = string.replace(" ! ", "! ") string = string.replace(" ? ", "? ") string = string.replace(" , ", ", ") # double brackets string = re.sub(r"\(\s*([^\)]*?)\s*\)", r"(\1)", string) string = re.sub(r"\[\s*([^\]]*?)\s*\]", r"[\1]", string) string = re.sub(r"{\s*([^}]*?)\s*}", r"{\1}", string) string = re.sub(r"\"\s*([^\"]*?)\s*\"", r'"\1"', string) string = re.sub(r"'\s*([^']*?)\s*'", r"'\1'", string) # miscellaneous string = string.replace("= = = =", "====") string = string.replace("= = =", "===") string = string.replace("= =", "==") string = string.replace(" " + chr(176) + " ", chr(176)) string = string.replace(" \n", "\n") string = string.replace("\n ", "\n") string = string.replace(" N ", " 1 ") string = string.replace(" 's", "'s") return string def lambada_detokenizer(string): return string _DETOKENIZERS = { 'ptb': ptb_detokenizer, 'wikitext': wikitext_detokenizer, 'lambada': lambada_detokenizer, } def get_detokenizer(path): for key in _DETOKENIZERS.keys(): if key in path: return _DETOKENIZERS[key] pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tasks/zeroshot_gpt2/evaluate.py000066400000000000000000000145351443434453700300670ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """GPT2 zero-shot evaluation.""" import math import torch from megatron import get_args from megatron import print_rank_0 from megatron import get_tokenizer from megatron import mpu from megatron.checkpointing import load_checkpoint from megatron.model import GPT2Model from megatron.training import get_model from megatron.utils import get_ltor_masks_and_position_ids from tasks.finetune_utils import build_data_loader from .datasets import build_dataset def get_model_provider(eval_metric): """Based on evaluation metric set the parallel-output flag and return the model provider.""" def model_provider(): """Build the model.""" if eval_metric == 'loss': parallel_output = True elif eval_metric == 'accuracy': parallel_output = False else: raise NotImplementedError('output type for {} evaluation metric ' 'is not supported.'.format(eval_metric)) print_rank_0('building GPT2 model ...') model = GPT2Model(num_tokentypes=0, parallel_output=parallel_output) return model return model_provider def process_batch(batch): """Process batch and produce inputs for the model.""" args = get_args() tokenizer = get_tokenizer() loss_mask = batch['pad_mask'].long().cuda().contiguous().byte() tokens_ = batch['text'].long().cuda().contiguous() labels = tokens_[:, 1:].contiguous() tokens = tokens_[:, :-1].contiguous() # Get the masks and postition ids. attention_mask, _, position_ids = get_ltor_masks_and_position_ids( tokens, tokenizer.eod, args.reset_position_ids, args.reset_attention_mask, args.eod_mask_loss) return tokens, labels, attention_mask, position_ids, loss_mask def forward_step(batch, model, eval_metric): """Forward step.""" # Get the batch. tokens, labels, attention_mask, position_ids, loss_mask = process_batch( batch) # Forward model. output = model(tokens, position_ids, attention_mask) # For loss, return the unreduced loss. if eval_metric == 'loss': losses = mpu.vocab_parallel_cross_entropy( output.contiguous().float(), labels.contiguous()) loss = torch.sum( losses.view(-1) * loss_mask.contiguous().view(-1).float()) return loss # For accuracy, return the number of correctly predicted samples. if eval_metric == 'accuracy': outputs = torch.argmax(output, -1) correct = (outputs == labels).float() correct[(1 - loss_mask).bool()] = 1 correct = correct.prod(-1) return correct.sum() raise NotImplementedError('forward method for evaluation metric {} ' 'is not implemented.'.format(eval_metric)) def evaluate(data_loader, model, eval_metric): """Evaluation.""" args = get_args() # Turn on evaluation mode which disables dropout. model.eval() total_output = 0.0 with torch.no_grad(): # For all the batches in the dataset. for iteration, batch in enumerate(data_loader): if iteration % args.log_interval == 0: print_rank_0('> working on iteration: {}'.format(iteration)) # Forward evaluation. output = forward_step(batch, model, eval_metric) # Reduce across processes. torch.distributed.all_reduce(output, group=mpu.get_data_parallel_group()) total_output += output return total_output def evaluate_and_print_results(task, data_loader, model, eval_metric): """Evaluate and print results on screen.""" # Evaluate and get results. output = evaluate(data_loader, model, eval_metric) string = ' validation results on {} | '.format(task) if eval_metric == 'loss': num_tokenized_tokens = data_loader.dataset.num_tokenized_tokens num_original_tokens = data_loader.dataset.num_original_tokens val_loss = output / (num_tokenized_tokens - 1) ppl = math.exp(min(20, val_loss)) token_ratio = (num_tokenized_tokens - 1) / (num_original_tokens - 1) adjusted_ppl = math.exp(min(20, val_loss * token_ratio)) string += 'avg loss: {:.4E} | '.format(val_loss) string += 'ppl: {:.4E} | '.format(ppl) string += 'adjusted ppl: {:.4E} | '.format(adjusted_ppl) string += 'token ratio: {} |'.format(token_ratio) elif eval_metric == 'accuracy': num_examples = len(data_loader.dataset) acc = output / num_examples string += 'number correct: {:.4E} | '.format(output) string += 'total examples: {:.4E} | '.format(num_examples) string += 'avg accuracy: {:.4E}'.format(acc) else: raise NotImplementedError('evaluation method for {} metric is not ' 'implemented yet.'.format(eval_metric)) length = len(string) + 1 print_rank_0('-' * length) print_rank_0(string) print_rank_0('-' * length) def main(): """Main program.""" args = get_args() if args.task == 'LAMBADA': eval_metric = 'accuracy' elif args.task == 'WIKITEXT103': eval_metric = 'loss' else: raise NotImplementedError('{} task is not implemented.'.format( args.task)) # Set up model and load checkpoint. model = get_model(get_model_provider(eval_metric)) if args.load is not None: _ = load_checkpoint(model, None, None) # Data stuff. dataset = build_dataset(args.task) dataloader = build_data_loader(dataset, args.batch_size, args.num_workers, drop_last=False) # Run evaluation. evaluate_and_print_results(args.task, dataloader, model, eval_metric) print_rank_0('done :-)') pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/test/000077500000000000000000000000001443434453700227205ustar00rootroot00000000000000pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/test/check_splited_merged_ckpt.py000066400000000000000000000021001443434453700304300ustar00rootroot00000000000000import torch def get_dict(dict1, res): if type(dict1) == type({}): for key in dict1: get_dict(dict1[key], res) else: try: for key in dict1: res.append(dict1[key]) except: print(dict1) pass if __name__ == '__main__': checkpoint_name = '/userhome/model/checkPoints/megatron-1.1-pangu-2.6B/partitions_splited/merged/iter_0007800/mp_rank_00/model_optim_rng.pt' sd1 = torch.load(checkpoint_name, map_location='cpu') checkpoint_name = '/userhome/model/checkPoints/megatron-1.1-pangu-2.6B/merged_v3/iter_0001000/mp_rank_00/model_optim_rng.pt' sd2 = torch.load(checkpoint_name, map_location='cpu') res1 = [] get_dict(sd1, res1) res2 = [] get_dict(sd2, res2) res = [] for i in range(len(res1)): tmp = torch.sub(res1[i].cuda(), res2[i].cuda()) tmp_max = torch.max(tmp) tmp_min = torch.min(tmp) res.append(float(tmp_max.cpu())) res.append(float(tmp_min.cpu())) pass a = min(res) b = max(res) passpcl_pangu/pcl_pangu/model/panguAlpha_pytorch/test/compare_tensor_mindspore_gpu.py000066400000000000000000000003171443434453700312460ustar00rootroot00000000000000import torch def compare(a): a = a.cpu() import numpy as np b = np.load('/userhome/tmp/tmp.npy') b = torch.Tensor(b).view_as(a) res = torch.max(torch.abs(torch.sub(a,b))) return res pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/test/valifier_mp.py000066400000000000000000000013551443434453700255730ustar00rootroot00000000000000import torch if __name__ == '__main__': full_model = torch.load(f"/userhome/tmp/mp_test/" f"model parallel {1}" f"_rank{0}" f"_lyaer{0}" f"_hidden_states.pth") rank0_model = torch.load(f"/userhome/tmp/mp_test/" f"model parallel {2}" f"_rank{0}" f"_lyaer{0}" f"_hidden_states.pth") rank1_model = torch.load(f"/userhome/tmp/mp_test/" f"model parallel {2}" f"_rank{1}" f"_lyaer{0}" f"_hidden_states.pth") print(full_model[0]) print(rank0_model[0]) print(rank1_model[0]) passpcl_pangu/pcl_pangu/model/panguAlpha_pytorch/testLayerNorm.py000066400000000000000000000017331443434453700251270ustar00rootroot00000000000000import torch class LayerNorm_(torch.nn.Module): def __init__(self, normalized_shape, init_method, dp=4, eps=1e-5, scale=1e-3): super(LayerNorm_, self).__init__() # Initialize master weight self.gamma = torch.empty(normalized_shape, dtype=torch.float, requires_grad=True) init_method(self.gamma) self.beta = torch.empty(normalized_shape, dtype=torch.float, requires_grad=True) init_method(self.beta) self.eps = eps def construct(self, x): mean = torch.mean(x, -1, keepdim=True) diff = torch.sub(x, mean) variance = torch.mean(torch.square(diff), -1,keepdim=True) variance_eps = torch.sqrt(torch.add(variance, self.eps)) output_ = torch.div(diff, variance_eps) output = torch.add(torch.mul(output_, self.gamma), self.beta) return output pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tools/000077500000000000000000000000001443434453700231015ustar00rootroot00000000000000pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tools/change_MspCkpt_ToMgt.py000066400000000000000000000372561443434453700274700ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Merge model parallel partitions.""" import os import sys from megatron.initialize import initialize_megatron sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir))) import numpy as np import torch from megatron import mpu from megatron.checkpointing import ensure_directory_exists from megatron.checkpointing import get_checkpoint_name from megatron.checkpointing import get_checkpoint_tracker_filename from megatron.global_vars import rebuild_tokenizer from megatron.global_vars import _parse_args # os.environ["CUDA_VISIBLE_DEVICES"] = "4,5,6,7" # os.system("export CUDA_VISIBLE_DEVICES=4,5,6,7") ######################### # --model-type GPT2 --model-parallel-size 2 --tokenizer-type GPT2BPETokenizer --vocab-file /ghome/yands/pclproject/gpt/Megatron-LM-1.1/megatron/tokenizer/bpe_4w_pcl/vocab --num-layers 24 --hidden-size 1024 --num-attention-heads 16 --seq-length 1024 --max-position-embeddings 1024 --load /ghome/yands/model/checkPoints/megatron-1.1-pangu # --model-type GPT2 --model-parallel-size 4 --tokenizer-type GPT2BPETokenizer --vocab-file /ghome/yands/pclproject/gpt/Megatron-LM-1.1/megatron/tokenizer/bpe_4w_pcl/vocab --num-layers 31 --hidden-size 2560 --num-attention-heads 32 --seq-length 1024 --max-position-embeddings 1024 --load /ghome/yands/model/checkPoints/megatron-1.1-pangu-2.6B ######################### def get_model(model_type): if model_type == 'BERT': from pretrain_bert import model_provider elif model_type == 'GPT2': from pretrain_gpt2 import model_provider elif model_type == 'Pangu': from pretrain_gpt2 import model_provider elif model_type == 'RACE': from tasks.race.finetune import model_provider elif model_type == ['MNLI', 'QQP']: num_classes = 2 if model_type == 'MNLI': num_classes = 3 from megatron.model.classification import Classification def model_provider(): return Classification(num_classes=num_classes, num_tokentypes=2) else: raise Exception('unrecognized model type: {}'.format(model_type)) model = model_provider() model = model.half() return model def get_change_ckpt_args(parser): """Provide extra arguments required for merging.""" group = parser.add_argument_group(title='mp merge') group.add_argument('--model-type', type=str, required=True, choices=['BERT', 'GPT2', 'Pangu', 'RACE', 'MNLI', 'QQP'], help='Type of the mdoel.') group.add_argument('--npy-ckpt-path', type=str, required=True, help='path of npy checkpoint.') return parser def loadModelFromNp(sd, args): num_layers = args.num_layers npCkptPath = args.npy_ckpt_path languageModel = sd['model']['language_model'] loadEmbeddingFromNp(npCkptPath, languageModel) transformer = sd['model']['language_model']['transformer'] for layerID in range(num_layers): loadAttentionLayerFromNp(npCkptPath, transformer, layerID) loadQueryLayerFromNp(npCkptPath, transformer) transformer['final_layernorm.weight'][:] = \ torch.tensor( np.load(npCkptPath+f'backbone.layernorm.gamma.npy') ).float() transformer['final_layernorm.bias'][:] = \ torch.tensor( np.load(npCkptPath+f'backbone.layernorm.beta.npy') ).float() def loadEmbeddingFromNp(npCkptPath, languageModel, vocabSize=40000): word_embedding_np = \ np.load(npCkptPath+'backbone.word_embedding.embedding_table.npy') languageModel['embedding']['word_embeddings']['weight'][:vocabSize,:] = \ torch.tensor(word_embedding_np).float() position_embeddings_np = \ np.load(npCkptPath+'backbone.position_embedding.embedding_table.npy') languageModel['embedding']['position_embeddings']['weight'][:,:] = \ torch.tensor(position_embeddings_np).float() topQueryEmbedding_np = \ np.load(npCkptPath+'backbone.top_query_embedding.embedding_table.npy') languageModel['topQueryEmbedding']['top_query_embeddings']['weight'][:,:] = \ torch.tensor(topQueryEmbedding_np).float() def loadAttentionLayerFromNp(npCkptPath, transformer, layerID): attention_dense1_weight_np = \ np.load(npCkptPath+f'backbone.blocks.{layerID}.attention.dense1.weight.npy') attention_dense2_weight_np = \ np.load(npCkptPath+f'backbone.blocks.{layerID}.attention.dense2.weight.npy') attention_dense3_weight_np = \ np.load(npCkptPath+f'backbone.blocks.{layerID}.attention.dense3.weight.npy') attention_dense1_bias_np = \ np.load(npCkptPath+f'backbone.blocks.{layerID}.attention.dense1.bias.npy') attention_dense2_bias_np = \ np.load(npCkptPath+f'backbone.blocks.{layerID}.attention.dense2.bias.npy') attention_dense3_bias_np = \ np.load(npCkptPath+f'backbone.blocks.{layerID}.attention.dense3.bias.npy') query_weight = transformer[f'layers.{layerID}.attention.query.weight'] key_weight = transformer[f'layers.{layerID}.attention.key.weight'] value_weight = transformer[f'layers.{layerID}.attention.value.weight'] query_weight[:] = torch.tensor(attention_dense1_weight_np).float() key_weight[:] = torch.tensor(attention_dense2_weight_np).float() value_weight[:] = torch.tensor(attention_dense3_weight_np).float() query_bias = transformer[f'layers.{layerID}.attention.query.bias'] key_bias = transformer[f'layers.{layerID}.attention.key.bias'] value_bias = transformer[f'layers.{layerID}.attention.value.bias'] query_bias[:] = torch.tensor(attention_dense1_bias_np).float() key_bias[:] = torch.tensor(attention_dense2_bias_np).float() value_bias[:] = torch.tensor(attention_dense3_bias_np).float() att_dense_weight = transformer[f'layers.{layerID}.attention.dense.weight'] att_dense_weight[:,:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.attention.projection.weight.npy').transpose() ).float() att_dense_bias = transformer[f'layers.{layerID}.attention.dense.bias'] att_dense_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.attention.projection.bias.npy') ).float() mlp_dense_h_to_4h_weight = transformer[f'layers.{layerID}.mlp.dense_h_to_4h.weight'] mlp_dense_h_to_4h_weight[:,:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.output.mapping.weight.npy').transpose() ).float() mlp_dense_h_to_4h_bias = transformer[f'layers.{layerID}.mlp.dense_h_to_4h.bias'] mlp_dense_h_to_4h_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.output.mapping.bias.npy') ).float() mlp_dense_4h_to_h_weight = transformer[f'layers.{layerID}.mlp.dense_4h_to_h.weight'] mlp_dense_4h_to_h_weight[:,:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.output.projection.weight.npy').transpose() ).float() mlp_dense_4h_to_h_bias = transformer[f'layers.{layerID}.mlp.dense_4h_to_h.bias'] mlp_dense_4h_to_h_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.output.projection.bias.npy') ).float() input_layernorm_weight = transformer[f'layers.{layerID}.input_layernorm.weight'] input_layernorm_weight[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.layernorm1.gamma.npy') ).float() input_layernorm_bias = transformer[f'layers.{layerID}.input_layernorm.bias'] input_layernorm_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.layernorm1.beta.npy') ).float() post_attention_layernorm_weight = transformer[f'layers.{layerID}.post_attention_layernorm.weight'] post_attention_layernorm_weight[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.layernorm2.gamma.npy') ).float() post_attention_layernorm_bias = transformer[f'layers.{layerID}.post_attention_layernorm.bias'] post_attention_layernorm_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.layernorm2.beta.npy') ).float() input_layernorm_weight = transformer[f'layers.{layerID}.input_layernorm.weight'] input_layernorm_weight[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.layernorm1.gamma.npy') ).float() input_layernorm_bias = transformer[f'layers.{layerID}.input_layernorm.bias'] input_layernorm_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.layernorm1.beta.npy') ).float() post_attention_layernorm_weight = transformer[f'layers.{layerID}.post_attention_layernorm.weight'] post_attention_layernorm_weight[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.layernorm2.gamma.npy') ).float() post_attention_layernorm_bias = transformer[f'layers.{layerID}.post_attention_layernorm.bias'] post_attention_layernorm_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.layernorm2.beta.npy') ).float() def loadQueryLayerFromNp(npCkptPath, transformer): attention_dense1_weight_np = \ np.load(npCkptPath+f'backbone.top_query_layer.attention.dense1.weight.npy') attention_dense1_bias_np = \ np.load(npCkptPath+f'backbone.top_query_layer.attention.dense1.bias.npy') attention_dense2_weight_np = \ np.load(npCkptPath+f'backbone.top_query_layer.attention.dense2.weight.npy') attention_dense2_bias_np = \ np.load(npCkptPath+f'backbone.top_query_layer.attention.dense2.bias.npy') attention_dense3_weight_np = \ np.load(npCkptPath+f'backbone.top_query_layer.attention.dense3.weight.npy') attention_dense3_bias_np = \ np.load(npCkptPath+f'backbone.top_query_layer.attention.dense3.bias.npy') query_weight = transformer[f'topQueryLayer.attention.query.weight'] query_weight[:, :] = \ torch.tensor(attention_dense1_weight_np).float() query_bias = transformer[f'topQueryLayer.attention.query.bias'] query_bias[:] = torch.tensor(attention_dense1_bias_np).float() key_weight = transformer[f'topQueryLayer.attention.key.weight'] key_weight[:, :] = \ torch.tensor(attention_dense2_weight_np).float() key_bias = transformer[f'topQueryLayer.attention.key.bias'] key_bias[:] = torch.tensor(attention_dense2_bias_np).float() value_weight = transformer[f'topQueryLayer.attention.value.weight'] value_weight[:, :] = \ torch.tensor(attention_dense3_weight_np).float() value_bias = transformer[f'topQueryLayer.attention.value.bias'] value_bias[:] = torch.tensor(attention_dense3_bias_np).float() att_dense_weight = transformer[f'topQueryLayer.attention.dense.weight'] att_dense_weight[:,:] = \ torch.tensor( np.load(npCkptPath+f'backbone.top_query_layer.attention.projection.weight.npy') .transpose() ).float() att_dense_bias = transformer[f'topQueryLayer.attention.dense.bias'] att_dense_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.top_query_layer.attention.projection.bias.npy') ).float() mlp_dense_h_to_4h_weight = transformer[f'topQueryLayer.mlp.dense_h_to_4h.weight'] mlp_dense_h_to_4h_weight[:,:] = \ torch.tensor( np.load(npCkptPath+f'backbone.top_query_layer.output.mapping.weight.npy') .transpose() ).float() mlp_dense_h_to_4h_bias = transformer[f'topQueryLayer.mlp.dense_h_to_4h.bias'] mlp_dense_h_to_4h_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.top_query_layer.output.mapping.bias.npy') ).float() mlp_dense_4h_to_h_weight = transformer[f'topQueryLayer.mlp.dense_4h_to_h.weight'] mlp_dense_4h_to_h_weight[:,:] = \ torch.tensor( np.load(npCkptPath+f'backbone.top_query_layer.output.projection.weight.npy') .transpose() ).float() mlp_dense_4h_to_h_bias = transformer[f'topQueryLayer.mlp.dense_4h_to_h.bias'] mlp_dense_4h_to_h_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.top_query_layer.output.projection.bias.npy') ).float() input_layernorm_weight = transformer[f'topQueryLayer.input_layernorm.weight'] input_layernorm_weight[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.top_query_layer.layernorm1.gamma.npy') ).float() input_layernorm_bias = transformer[f'topQueryLayer.input_layernorm.bias'] input_layernorm_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.top_query_layer.layernorm1.beta.npy') ).float() post_attention_layernorm_weight = transformer[f'topQueryLayer.post_attention_layernorm.weight'] post_attention_layernorm_weight[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.top_query_layer.layernorm2.gamma.npy') ).float() post_attention_layernorm_bias = transformer[f'topQueryLayer.post_attention_layernorm.bias'] post_attention_layernorm_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.top_query_layer.layernorm2.beta.npy') ).float() def main(): # test_split_merge() # Args args = _parse_args(extra_args_provider=get_change_ckpt_args) model_type = args.model_type # orig_model_parallel_size = args.model_parallel_size args.model_parallel_size = 1 tokenizer = rebuild_tokenizer(args) print('\n merging model parallel partitions ...') print(' > checkpoint path: {}'.format(args.load)) print(' > model parameters:') print(' number of tokens ................ {} '.format( tokenizer.vocab_size)) print(' number of layers ................ {}'.format(args.num_layers)) print(' hidden sise ..................... {}'.format(args.hidden_size)) print(' number of attention heads ....... {}'.format( args.num_attention_heads)) print(' maximum position embeddings ..... {}'.format( args.max_position_embeddings)) # Full model. print('> building the full model ...') mpu.initialize.set_model_parallel_world_size(1) mpu.initialize.set_model_parallel_rank(0) # initialize_megatron(args_defaults={'tokenizer_type': 'GPT2BPETokenizer'}) merged_model = get_model(model_type) iteration = 1000 #any num is ok # Save the model. args.model_parallel_size = 1 mpu.initialize.set_model_parallel_rank(0) sd = {} sd['model'] = merged_model.state_dict_for_save_checkpoint() sd['iteration'] = iteration merged_path = os.path.join(args.npy_ckpt_path, 'merged') checkpoint_name = get_checkpoint_name(merged_path, iteration) ensure_directory_exists(checkpoint_name) loadModelFromNp(sd, args) print('> saving merged model to {}'.format(checkpoint_name)) torch.save(sd, checkpoint_name) recordFile = open(get_checkpoint_tracker_filename(merged_path), 'w') recordFile.writelines(str(iteration)) recordFile.close() print('done :-)') if __name__ == '__main__': main() pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tools/create_doc_index.py000066400000000000000000000016171443434453700267370ustar00rootroot00000000000000import sys sys.path.append('../') from megatron.indexer import IndexBuilder from megatron.initialize import initialize_megatron def main(): """Create a BlockData data structure by running an IndexBuilder over an ICT Dataset - Include all args needed for initial model specification Other key args: --block-data-path: path to write to --ict-load or --realm-load: path to checkpoint with which to embed --data-path and --titles-data-path: paths for dataset --indexer-log-interval: reporting interval --indexer-batch-size: size specific for indexer jobs Check README_mgt.md for example script """ initialize_megatron(extra_args_provider=None, args_defaults={'tokenizer_type': 'BertWordPieceLowerCase'}) index_builder = IndexBuilder() index_builder.build_and_save_index() if __name__ == "__main__": main() pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tools/generate_samples_Pangu.py000066400000000000000000000247171443434453700301360ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Sample Generate GPT2""" import os import sys import numpy as np import torch sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir))) from megatron.text_generation_utils import pad_batch, get_batch from megatron import get_args from megatron import print_rank_0 from megatron import get_tokenizer from megatron.checkpointing import load_checkpoint from megatron.initialize import initialize_megatron from megatron.model import GPT2Model from megatron.training import get_model from megatron.text_generation_utils import generate_and_write_samples_unconditional from megatron.text_generation_utils import generate_samples_input_from_file from megatron.text_generation_utils import generate_samples_interactive import torch.nn.functional as F # from megatron.model.transformer import LayerNorm def model_provider(): """Build the model.""" print_rank_0('building GPT2 model ...') model = GPT2Model(num_tokentypes=0, parallel_output=False) return model def add_text_generate_args(parser): """Text generation arguments.""" group = parser.add_argument_group(title='text generation') group.add_argument("--isEvolution", type=bool, default=False, help='if using Evolution model') group.add_argument("--temperature", type=float, default=1.0, help='Sampling temperature.') group.add_argument("--greedy", action='store_true', default=False, help='Use greedy sampling.') group.add_argument("--top-p", type=float, default=0.0, help='Top p sampling.') group.add_argument("--top-k", type=int, default=5, help='Top k sampling.') group.add_argument("--out-seq-length", type=int, default=1024, help='Size of the output generated text.') group.add_argument("--generate_max_tokens", type=int, default=128, help='generate_max_tokens from (0, 800]') group.add_argument("--sample-input", type=str, default=None, help='Input one sample to generate.') group.add_argument("--sample-input-file", type=str, default=None, help='Get input from file instead of interactive mode, ' 'each line is an input.') group.add_argument("--sample-output-file", type=str, default=None, help='Output file got from --sample-input-file') group.add_argument("--num-samples", type=int, default=0, help='Number of samples to generate unconditionally, ' 'defaults to 0 and interactive conditional sampling') group.add_argument("--genfile", type=str, help='Output file when generating unconditionally') group.add_argument("--recompute", action='store_true', help='During generation recompute all attention ' 'instead of using previously computed keys/values.') return parser def top_k_logits(logits, top_k=0, top_p=0.9, filter_value=-float('Inf')): """ This function has been mostly taken from huggingface conversational ai code at https://medium.com/huggingface/how-to-build-a-state-of-the-art- conversational-ai-with-transfer-learning-2d818ac26313 """ if top_k > 0: # Remove all tokens with a probability less than the last token of the top-k indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None] logits[indices_to_remove] = filter_value batch_size = logits.size()[0] if top_p > 0.0: logits = logits.view(batch_size, -1).contiguous() for logit in logits: sorted_logits, sorted_indices = torch.sort(logit, descending=True) cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) # Remove tokens with cumulative probability above the threshold sorted_indices_to_remove = cumulative_probs > top_p # Shift the indices to the right to keep also the first token above the threshold sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone() sorted_indices_to_remove[..., 0] = 0 indices_to_remove = sorted_indices[sorted_indices_to_remove] logit[indices_to_remove] = filter_value logits = logits.view(batch_size, -1).contiguous() return logits def generate(model, context_tokens, args, tokenizer, max_num=50): valid_length = len(context_tokens) context_tokens_, context_lengths = pad_batch([context_tokens], tokenizer.pad_id, args) context_tokens_tensor = torch.cuda.LongTensor(context_tokens_) tokens, attention_mask, position_ids = get_batch(context_tokens_tensor) type_ids = None bs,_ = tokens.shape cnt = 0 while valid_length < args.seq_length: with torch.no_grad(): logits = model(tokens, position_ids, attention_mask, tokentype_ids=type_ids, forward_method_parallel_output=False) logits = logits[:,:,:tokenizer.vocab_size].cpu() logits = logits.numpy() logits = logits.reshape(bs, args.seq_length, -1) probs = logits[0, valid_length-1, :] p_args = probs.argsort()[::-1][:args.top_k] p = probs[p_args] p = p / sum(p) for i in range(1000): target_index = np.random.choice(len(p), p=p) if p_args[target_index] != tokenizer.unk: break if p_args[target_index] == tokenizer.eod or \ valid_length == args.seq_length-1 or cnt>=max_num: outputs = tokens.cpu().numpy() break tokens[0][valid_length] = p_args[target_index] valid_length += 1 cnt += 1 length = np.sum(outputs != tokenizer.pad_id) outputs = outputs[0][:length] return outputs def generate_samples_cftpd(model, context_tokens, args, tokenizer, max_num=50, top_k=0, top_p=0.9, temperature=1.0): valid_length = len(context_tokens) if valid_length > args.seq_length - max_num: valid_length = args.seq_length - max_num context_tokens = context_tokens[-valid_length:] context_tokens_, context_lengths = pad_batch([context_tokens], tokenizer.pad_id, args) context_tokens_tensor = torch.cuda.LongTensor(context_tokens_) tokens, attention_mask, position_ids = get_batch(context_tokens_tensor) type_ids = None bs, _ = tokens.shape cnt = 0 with torch.no_grad(): while valid_length < args.seq_length: logits = model(tokens, position_ids, attention_mask, tokentype_ids=type_ids, forward_method_parallel_output=False) probs = logits[:, valid_length - 1, :] / temperature probs = top_k_logits(probs, top_k=top_k, top_p=top_p) probs = F.softmax(probs, dim=-1) target_index = torch.multinomial(probs.float(), num_samples=1).squeeze(1) if target_index == tokenizer.eod or valid_length == args.seq_length - 1 or cnt >= max_num: outputs = tokens break tokens[0][valid_length] = target_index valid_length += 1 cnt += 1 length = torch.sum(outputs != tokenizer.pad_id) outputs = outputs[0][:length] return outputs def main(): """Main program.""" initialize_megatron(extra_args_provider=add_text_generate_args, args_defaults={'tokenizer_type': 'GPT2BPETokenizer'}) args = get_args() # Set up model and load checkpoint. model = get_model(model_provider) model.eval() assert args.top_k >= 1, "> reset top_k from [1, 512], bigger the value, richer the generation diversity!" assert args.top_p > 0.0, "> reset top_p from [0.1, 1), bigger the value, richer the generation diversity!" assert args.top_p < 1.0, "> reset top_p from [0.1, 1), bigger the value, richer the generation diversity!" if args.load is not None: _ = load_checkpoint(model, None, None) if args.sample_input is not None: raw_text = bytes.fromhex(args.sample_input).decode('utf-8') tokenizer = get_tokenizer() context_tokens = tokenizer.tokenize(raw_text) # output_ids = generate(model, context_tokens, args, tokenizer) output_ids = generate_samples_cftpd(model, context_tokens, args, tokenizer, max_num=args.generate_max_tokens, top_k=args.top_k, top_p=args.top_p) output_samples = tokenizer.convert_ids_to_tokens(output_ids.tolist()) print('Input is:', raw_text) print('Output is:', output_samples[len(raw_text):], flush=True) print() if args.sample_input_file is not None: raw_texts = open(args.sample_input_file, 'r').read().split('\n\n') write_output = print if args.sample_output_file is not None: output_file = open(args.sample_output_file, 'w') write_output = lambda x: output_file.write(x + '\n') tokenizer = get_tokenizer() for raw_text in raw_texts: context_tokens = tokenizer.tokenize(raw_text) # output_ids = generate(model, context_tokens, args, tokenizer) output_ids = generate_samples_cftpd(model, context_tokens, args, tokenizer, max_num=args.generate_max_tokens, top_k=args.top_k, top_p=args.top_p) output_samples = tokenizer.convert_ids_to_tokens(output_ids.tolist()) write_output('Input is: ' + raw_text) write_output('Output is: ' + ' '.join(output_samples[len(raw_text):])) write_output() if __name__ == "__main__": main() pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tools/generate_samples_epangu.py000066400000000000000000000245071443434453700303400ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Sample Generate GPT2""" import os import sys import numpy as np import torch sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir))) from megatron.text_generation_utils import pad_batch, get_batch from megatron import get_args from megatron import print_rank_0 from megatron import get_tokenizer from megatron.checkpointing import load_checkpoint from megatron.initialize import initialize_megatron from megatron.model import EnhancedModel from megatron.training import get_model from megatron.text_generation_utils import generate_and_write_samples_unconditional from megatron.text_generation_utils import generate_samples_input_from_file from megatron.text_generation_utils import generate_samples_interactive import torch.nn.functional as F # from megatron.model.transformer import LayerNorm def model_provider(): """Build the model.""" print_rank_0('building GPT2 model ...') model = EnhancedModel(num_tokentypes=0, parallel_output=False) return model def add_text_generate_args(parser): """Text generation arguments.""" group = parser.add_argument_group(title='text generation') group.add_argument("--temperature", type=float, default=1.0, help='Sampling temperature.') group.add_argument("--greedy", action='store_true', default=False, help='Use greedy sampling.') group.add_argument("--top-p", type=float, default=0.0, help='Top p sampling.') group.add_argument("--top-k", type=int, default=5, help='Top k sampling.') group.add_argument("--generate_max_tokens", type=int, default=128, help='generate_max_tokens from (0, 800]') group.add_argument("--out-seq-length", type=int, default=1024, help='Size of the output generated text.') group.add_argument("--sample-input", type=str, default=None, help='Input one sample to generate.') group.add_argument("--sample-input-file", type=str, default=None, help='Get input from file instead of interactive mode, ' 'each line is an input.') group.add_argument("--sample-output-file", type=str, default=None, help='Output file got from --sample-input-file') group.add_argument("--num-samples", type=int, default=0, help='Number of samples to generate unconditionally, ' 'defaults to 0 and interactive conditional sampling') group.add_argument("--genfile", type=str, help='Output file when generating unconditionally') group.add_argument("--recompute", action='store_true', help='During generation recompute all attention ' 'instead of using previously computed keys/values.') return parser def top_k_logits(logits, top_k=0, top_p=0.9, filter_value=-float('Inf')): """ This function has been mostly taken from huggingface conversational ai code at https://medium.com/huggingface/how-to-build-a-state-of-the-art- conversational-ai-with-transfer-learning-2d818ac26313 """ if top_k > 0: # Remove all tokens with a probability less than the last token of the top-k indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None] logits[indices_to_remove] = filter_value batch_size = logits.size()[0] if top_p > 0.0: logits = logits.view(batch_size, -1).contiguous() for logit in logits: sorted_logits, sorted_indices = torch.sort(logit, descending=True) cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) # Remove tokens with cumulative probability above the threshold sorted_indices_to_remove = cumulative_probs > top_p # Shift the indices to the right to keep also the first token above the threshold sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone() sorted_indices_to_remove[..., 0] = 0 indices_to_remove = sorted_indices[sorted_indices_to_remove] logit[indices_to_remove] = filter_value logits = logits.view(batch_size, -1).contiguous() return logits def generate(model, context_tokens, args, tokenizer, max_num=50): valid_length = len(context_tokens) context_tokens_, context_lengths = pad_batch([context_tokens], tokenizer.pad_id, args) context_tokens_tensor = torch.cuda.LongTensor(context_tokens_) tokens, attention_mask, position_ids = get_batch(context_tokens_tensor) type_ids = None bs,_ = tokens.shape cnt = 0 while valid_length < args.seq_length: with torch.no_grad(): logits = model(tokens, position_ids, attention_mask, tokentype_ids=type_ids, forward_method_parallel_output=False) logits = logits[:,:,:tokenizer.vocab_size].cpu() logits = logits.numpy() logits = logits.reshape(bs, args.seq_length, -1) probs = logits[0, valid_length-1, :] p_args = probs.argsort()[::-1][:args.top_k] p = probs[p_args] p = p / sum(p) for i in range(1000): target_index = np.random.choice(len(p), p=p) if p_args[target_index] != tokenizer.unk: break if p_args[target_index] == tokenizer.eod or \ valid_length == args.seq_length-1 or cnt>=max_num: outputs = tokens.cpu().numpy() break tokens[0][valid_length] = p_args[target_index] valid_length += 1 cnt += 1 length = np.sum(outputs != tokenizer.pad_id) outputs = outputs[0][:length] return outputs def generate_samples(model, context_tokens, args, tokenizer, max_num=50, top_k=0, top_p=0.9, temperature=1.0): valid_length = len(context_tokens) if valid_length > args.seq_length - max_num: valid_length = args.seq_length - max_num context_tokens = context_tokens[-valid_length:] context_tokens_, context_lengths = pad_batch([context_tokens], tokenizer.pad_id, args) context_tokens_tensor = torch.cuda.LongTensor(context_tokens_) tokens, attention_mask, position_ids = get_batch(context_tokens_tensor) type_ids = None bs, _ = tokens.shape cnt = 0 with torch.no_grad(): while valid_length < args.seq_length: logits = model(tokens, position_ids, attention_mask, tokentype_ids=type_ids, forward_method_parallel_output=False) probs = logits[:, valid_length - 1, :] / temperature probs = top_k_logits(probs, top_k=top_k, top_p=top_p) probs = F.softmax(probs, dim=-1) target_index = torch.multinomial(probs.float(), num_samples=1).squeeze(1) if target_index == tokenizer.eod or valid_length == args.seq_length - 1 or cnt >= max_num: outputs = tokens break tokens[0][valid_length] = target_index valid_length += 1 cnt += 1 length = torch.sum(outputs != tokenizer.pad_id) outputs = outputs[0][:length] return outputs def main(): """Main program.""" initialize_megatron(extra_args_provider=add_text_generate_args, args_defaults={'tokenizer_type': 'GPT2BPETokenizer'}) # Set up model and load checkpoint. model = get_model(model_provider) model.eval() args = get_args() assert args.top_k >= 1, "> reset top_k from [1, 512], bigger the value, richer the generation diversity!" assert args.top_p > 0.0, "> reset top_p from [0.1, 1), bigger the value, richer the generation diversity!" assert args.top_p < 1.0, "> reset top_p from [0.1, 1), bigger the value, richer the generation diversity!" if args.load is not None: _ = load_checkpoint(model, None, None) if args.sample_input is not None: raw_text = bytes.fromhex(args.sample_input).decode('utf-8') tokenizer = get_tokenizer() context_tokens = tokenizer.tokenize(raw_text) # output_ids = generate(model, context_tokens, args, tokenizer) output_ids = generate_samples(model, context_tokens, args, tokenizer, max_num=args.generate_max_tokens, top_k=args.top_k, top_p=args.top_p) output_samples = tokenizer.convert_ids_to_tokens(output_ids.tolist()) print('Input is:', raw_text) print('Output is:', output_samples[len(raw_text):], flush=True) print() if args.sample_input_file is not None: raw_texts = open(args.sample_input_file, 'r').read().split('\n\n') write_output = print if args.sample_output_file is not None: output_file = open(args.sample_output_file, 'w') write_output = lambda x: output_file.write(x + '\n') tokenizer = get_tokenizer() for raw_text in raw_texts: context_tokens = tokenizer.tokenize(raw_text) # output_ids = generate(model, context_tokens, args, tokenizer) output_ids = generate_samples(model, context_tokens, args, tokenizer, max_num=args.generate_max_tokens, top_k=args.top_k, top_p=args.top_p) output_samples = tokenizer.convert_ids_to_tokens(output_ids.tolist()) write_output('Input is: ' + raw_text) write_output('Output is: ' + ' '.join(output_samples[len(raw_text):])) write_output() if __name__ == "__main__": main() pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tools/linter.py000066400000000000000000000023531443434453700247530ustar00rootroot00000000000000import os import os.path as osp import pathlib import subprocess def recursively_lint_files(): """Recursively lint all python files in chosen subdirectories of megatron-lm""" try: import autopep8 except ModuleNotFoundError: print("Please first install autopep8 via `pip install autopep8`") return # get all python file paths from top level directory file_dir = str(pathlib.Path(__file__).parent.absolute()) working_dir = osp.join(file_dir, os.pardir) all_py_paths = set(os.path.join(working_dir, fname) for fname in os.listdir(working_dir) if ".py" in fname) # get all python file paths from chosen subdirectories check_dirs = ['docker', 'megatron', 'openwebtext', 'scripts', 'tasks'] for sub_dir in check_dirs: for path, _, fnames in os.walk(osp.join(working_dir, sub_dir)): all_py_paths.update(set(osp.join(path, fname) for fname in fnames if ".py" in fname)) print("Linting the following: ") for py_path in all_py_paths: print(py_path) command = 'autopep8 --max-line-length 100 --aggressive --in-place {}'.format(py_path) subprocess.check_call(command) if __name__ == "__main__": recursively_lint_files() pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tools/merge_mp_partitions.py000066400000000000000000000260741443434453700275330ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Merge model parallel partitions.""" import os import sys sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir))) import torch from megatron import mpu from megatron.checkpointing import ensure_directory_exists from megatron.checkpointing import get_checkpoint_name from megatron.checkpointing import get_checkpoint_tracker_filename from megatron.global_vars import rebuild_tokenizer from megatron.global_vars import _parse_args def split_into_partitions(tensor, num_partitions, partition_dim, stride): per_partition_size = mpu.utils.divide(tensor.size(partition_dim), num_partitions) per_partition_per_stride_size = mpu.utils.divide(per_partition_size, stride) partitions_list = torch.split(tensor, per_partition_per_stride_size, dim=partition_dim) partitions = [] for i in range(num_partitions): partition = torch.cat(partitions_list[i::num_partitions], dim=partition_dim) partitions.append(partition) return partitions def merge_partitions(merged, partitions, partition_dim, stride): # Number and size of each partition. num_partitions = len(partitions) per_partition_size = None for partition in partitions: if per_partition_size is None: per_partition_size = partition.size(partition_dim) else: assert per_partition_size == partition.size(partition_dim) def concat_partitions(partitions_): with torch.no_grad(): if (per_partition_size * num_partitions) == merged.size( partition_dim): torch.cat(partitions_, dim=partition_dim, out=merged) else: print(' ***WARNING*** sizes do not match. Will cut ' 'the merged partitions by {} along dimension {} ' 'to reduce the size from {} to {} ...'.format( (per_partition_size * num_partitions) - \ merged.size(partition_dim), partition_dim, per_partition_size * num_partitions, merged.size(partition_dim))) merged_ = torch.cat(partitions_, dim=partition_dim) merged_split = torch.split(merged_, merged.size(partition_dim), dim=partition_dim) merged_ = merged_split[0] assert merged_.size(partition_dim) == merged.size(partition_dim) merged.data.copy_(merged_.data) # If stride is 1, then do simple concatination. if stride == 1: concat_partitions(partitions) return # For none unity strides, first split based on stride and then group. per_partition_per_stride_size = mpu.utils.divide(per_partition_size, stride) # Chunk and build a list. chunks = None for i, partition in enumerate(partitions): chunk = torch.split(partition, per_partition_per_stride_size, dim=partition_dim) if chunks is None: chunks = [0]*(num_partitions*len(chunk)) chunks[i::num_partitions] = chunk # Concatinate. concat_partitions(chunks) return def get_model(model_type): if model_type == 'BERT': from pretrain_bert import model_provider elif model_type == 'GPT2': from pretrain_gpt2 import model_provider elif model_type == 'Pangu': from pretrain_gpt2 import model_provider elif model_type == 'RACE': from tasks.race.finetune import model_provider elif model_type == ['MNLI', 'QQP']: num_classes = 2 if model_type == 'MNLI': num_classes = 3 from megatron.model.classification import Classification def model_provider(): return Classification(num_classes=num_classes, num_tokentypes=2) else: raise Exception('unrecognized model type: {}'.format(model_type)) model = model_provider() model = model.half() return model def get_parallel_checkpoint_name(path): tracker_filename = get_checkpoint_tracker_filename(path) iteration = 0 with open(tracker_filename, 'r') as f: metastring = f.read().strip() iteration = int(metastring) assert iteration > 0 checkpoint_name = get_checkpoint_name(path, iteration) return checkpoint_name, iteration def test_split_merge(): print('testing split and merge ...') #[QKV.ROW-COL] tensor = torch.FloatTensor([[1.11, 1.12, 1.13, 1.14, 1.15], [1.21, 1.22, 1.23, 1.24, 1.25], [1.31, 1.32, 1.33, 1.34, 1.35], [1.41, 1.42, 1.43, 1.44, 1.45], [2.11, 2.12, 2.13, 2.14, 2.15], [2.21, 2.22, 2.23, 2.24, 2.25], [2.31, 2.32, 2.33, 2.34, 2.35], [2.41, 2.42, 2.43, 2.44, 2.45], [3.11, 3.12, 3.13, 3.14, 3.15], [3.21, 3.22, 3.23, 3.24, 3.25], [3.31, 3.32, 3.33, 3.34, 3.35], [3.41, 3.42, 3.43, 3.44, 3.45]]) num_partitions = 2 partition_dim = 0 stride = 1 partitions = split_into_partitions(tensor, num_partitions, partition_dim, stride) merged = torch.zeros_like(tensor) merge_partitions(merged, partitions, partition_dim, stride) max_error = (merged - tensor).abs().max() print(' > max error (should be zero): {}'.format(max_error)) def get_mp_merge_args(parser): """Provide extra arguments required for merging.""" group = parser.add_argument_group(title='mp merge') group.add_argument('--model-type', type=str, required=True, choices=['BERT', 'GPT2', 'Pangu', 'RACE', 'MNLI', 'QQP'], help='Type of the mdoel.') group.add_argument('--mp-model-parallel-size', type=int, required=True, help='number of mp model to be merged.') return parser def main(): # test_split_merge() # Args args = _parse_args(extra_args_provider=get_mp_merge_args) model_type = args.model_type orig_model_parallel_size = args.mp_model_parallel_size args.model_parallel_size = 1 tokenizer = rebuild_tokenizer(args) print('\n merging model parallel partitions ...') print(' > number of partitions: {}'.format(orig_model_parallel_size)) print(' > checkpoint path: {}'.format(args.load)) print(' > model parameters:') print(' number of tokens ................ {} '.format( tokenizer.vocab_size)) print(' number of layers ................ {}'.format(args.num_layers)) print(' hidden sise ..................... {}'.format(args.hidden_size)) print(' number of attention heads ....... {}'.format( args.num_attention_heads)) print(' maximum position embeddings ..... {}'.format( args.max_position_embeddings)) # Full model. print('> building the full model ...') mpu.initialize.set_model_parallel_world_size(1) mpu.initialize.set_model_parallel_rank(0) merged_model = get_model(model_type) # Build and load partitions. partitions = [] iteration = 0 args.model_parallel_size = orig_model_parallel_size tokenizer = rebuild_tokenizer(args) mpu.initialize.set_model_parallel_world_size(args.model_parallel_size) for rank in range(args.model_parallel_size): mpu.initialize.set_model_parallel_rank(rank) checkpoint_name, iteration = get_parallel_checkpoint_name(args.load) print('> loading {} ...'.format(checkpoint_name)) model_ = get_model(model_type) sd = torch.load(checkpoint_name, map_location='cpu') model_.load_state_dict(sd['model']) partitions.append(model_) # Parameter generators so we can loop through them semiltaneouly. merged_params_gen = merged_model.named_parameters() partitions_params_gen = [partition.named_parameters() for partition in partitions] while True: try: # Get the params and check names. name, merged_param = next(merged_params_gen) print(' > working on {} ...'.format(name)) print(' merged type: {}, size: {}'.format( merged_param.dtype, list(merged_param.size()))) partitions_param = [] for rank, partition_params_gen in enumerate(partitions_params_gen): partition_name, partition_param = next(partition_params_gen) assert partition_name == name partitions_param.append(partition_param) print(' partition {} type: {}, size: {}'.format( rank, partition_param.dtype, list(partition_param.size()))) # For the non-parallel parameters, simply copy the rank 0 values. if not hasattr(merged_param, 'model_parallel'): print(' none-parallel parameter, simple copy from rank 0') with torch.no_grad(): merged_param.data.copy_(partitions_param[0].data) # For parallel parameters, merge the values else: print(' parallel parameter merge with stride {} along ' 'dimention {}'.format(merged_param.stride, merged_param.partition_dim)) merge_partitions(merged_param, partitions_param, merged_param.partition_dim, merged_param.stride) except StopIteration: break # Save the model. args.model_parallel_size = 1 mpu.initialize.set_model_parallel_rank(0) sd = {} sd['model'] = merged_model.state_dict_for_save_checkpoint() sd['iteration'] = iteration merged_path = os.path.join(args.load, 'merged') checkpoint_name = get_checkpoint_name(merged_path, iteration) ensure_directory_exists(checkpoint_name) print('> saving merged model to {}'.format(merged_path)) torch.save(sd, checkpoint_name) recordFile = open(get_checkpoint_tracker_filename(merged_path), 'w') recordFile.writelines(str(iteration)) recordFile.close() print('done :-)') if __name__ == '__main__': main() pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tools/openwebtext/000077500000000000000000000000001443434453700254455ustar00rootroot00000000000000pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tools/openwebtext/README.md000066400000000000000000000037551443434453700267360ustar00rootroot00000000000000The following steps show how to prepare training dataset to train the mode. # Libraries to install ``` pip install ftfy langdetect numpy torch pandas nltk sentencepiece boto3 tqdm regex bs4 newspaper3k htmlmin tldextract git clone https://github.com/mattilyra/LSH cd LSH python setup.py install ``` # Download the dataset 1. Download the deduplicated URLs from [jcpeterson](https://mega.nz/#F!EZZD0YwJ!9_PlEQzdMVLaNdKv_ICNVQ!cc4RgQQZ) 2. Remove blacklisted URLs. ``` python blacklist_urls.py ``` 3. Download the content from the clean urls with [openwebtext's utilities](https://github.com/eukaryote31/openwebtext/blob/master/download.py). 4. Merge the contents into one loose json file with 1 json per newline of the format `{'text': text, 'url': unique_url}`. It is important for the url to be unique. # Prepare the data for GPT-2 training: 1. Perform ftfy, english detection and remove documents with less than 128 tokens. This step can be sharded and run on shards. ``` python cleanup_dataset.py ``` 2. Using LSH, find possible duplicates and store then in a file for later processing. This step can NOT be sharded and usually takes 12 to 24 hours for OpenWebText dataset. ``` python find_duplicates.py ``` 3. Based on similarity measure defind inside function `is_similar` (default: 0.9), group urls that are similar. Basically, for each group, only one url we should keep and remove the rest. ``` python group_duplicate_urls.py ``` 4. Remove similar documents that were detected in the last step. ``` python remove_group_duplicates.py ``` 5. Shuffle the dataset. ``` shuf -o train_data.json ``` pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tools/openwebtext/blacklist_urls.py000066400000000000000000000162601443434453700310410ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import glob import re import time import tldextract import sys # List of the domains to blacklist. domain_blacklist = set([ '500px', 'aapks', 'akamaihd', 'amazon', 'apple', 'artifactfire', 'artstation', 'awwni', 'bandcamp', 'battleforthenet', 'coinscalendar', 'dailymotion', 'deviantart', 'discord', 'discordapp', 'dlapkandroid', 'dropbox', 'e621', 'ebay', 'edealinfo', 'erome', 'eroshare', 'explosm', 'facebook', 'fbcdn', 'flickr', 'furaffinity', 'futhead', 'gatopardo', 'gfycat', 'gifsound', 'gifsoup', 'giphy', 'github', 'google', 'gunprime', 'gyazo', 'hotdealstar', 'imagefap', 'imageshack', 'imgflip', 'imgur', 'instagram', 'karmadecay', 'kryptocal', 'kym-cdn', 'liveleak', 'livememe', 'lmgtfy', 'magaimg', 'memegenerator', 'minorplanetcenter', 'minus', 'mobafire', 'morejpeg', 'nocookie', 'pcpartpicker', 'photobucket', 'pinimg', 'pinterest', 'pixiv', 'pornhub', 'prntscr', 'puu', 'qkme', 'quickmeme', 'radd', 'redd', 'reddit', 'reddit-stream', 'redditlog', 'redditmedia', 'reddituploads', 'redtube', 'reupp', 'reverb', 'roanoke', 'rollingstone', 'sli', 'soundcloud', 'soundgasm', 'spankbang', 'spotify', 'strawpoll', 'streamable', 'timeanddate', 'tinypic', 'touhouradio', 'tumblr', 'twimg', 'twitch', 'twitter', 'vid', 'vimeo', 'vine', 'vkaao', 'vocaroo', 'voyagefusion', 'walmart', 'wciu', 'wikimedia', 'wikipedia', 'xhamster', 'xkcd', 'xvideos', 'youtu', 'youtube', 'youtubedoubler', 'ytimg', 'zillexplorer', ]) def domain_is_in_blacklist(url): domain = tldextract.extract(url).domain return domain in domain_blacklist # List of extentions to blacklist. extentions_blacklist = ( '.3gp', '.7z' '.ai', '.aif', '.apk', '.app', '.avi', '.bin', '.bmp', '.bz2', '.css', '.csv', '.dat', '.deb', '.dmg', '.doc', '.docx', '.exe', '.gif', '.gifv', '.gz', '.iso', '.jar', '.jpeg', '.jpg', '.js', '.log', '.mid', '.midi', '.mkv', '.mov', '.mp3', '.mp4', '.mpeg', '.mpg', '.ogg', '.ogv', '.otf', '.pdf', '.pkg', '.png', '.pps', '.ppt', '.pptx', '.psd', '.py', '.qt', '.ram', '.rar', '.sql', '.svg', '.swf', '.tar.gz', '.tar', '.tgz', '.tiff', '.ttf', '.txt', '.wav', '.webm', '.wma', '.wmv', '.xls', '.xlsx', '.xml', '.xz', '.zip', ) def extention_is_in_blacklist(url): if url.split('?')[0].lower().endswith(extentions_blacklist): return True return False # Malformed urls. # This function is adapted from: # https://stackoverflow.com/questions/7160737/python-how-to-validate-a-url-in-python-malformed-or-not url_regex = re.compile( r'^(?:http)s?://' # http:// or https:// r'(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\.)+(?:[A-Z]{2,6}\.?|[A-Z0-9-]{2,}\.?)|' #domain... r'\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3})' # ...or ip r'(?::\d+)?' # optional port r'(?:/?|[/?]\S+)$', re.IGNORECASE) def url_is_malformed(url): return re.match(url_regex, url) is None def print_progress(prefix, start_time, urls_counter, domain_blacklist_counter, extention_blacklist_counter, short_url_counter, malformed_url_counter, duplicate_url_counter): string = prefix + ' | ' string += 'time elapsed (s): {:.2f} | '.format(time.time() - start_time) string += 'number of urls: {} | '.format(urls_counter) string += 'domain blacklisted: {} | '.format(domain_blacklist_counter) string += 'extention blacklisted: {} | '.format(extention_blacklist_counter) string += 'short urls (<=8): {} | '.format(short_url_counter) string += 'malformed urls: {} | '.format(malformed_url_counter) string += 'duplicate urls: {}'.format(duplicate_url_counter) print(string, flush=True) if __name__ == '__main__': print('remove blacklisted urls ..') # Path to the url files. path = sys.argv[1] # Output url file. output = sys.argv[2] # Get the list of url files. files = glob.glob(path + '/*.txt') print('> found {} files'.format(len(files))) urls = set() urls_counter = 0 domain_blacklist_counter = 0 extention_blacklist_counter = 0 short_url_counter = 0 malformed_url_counter = 0 duplicate_url_counter = 0 start_time = time.time() for filename in files: with open(filename, 'r') as f: for line in f: url = line.strip() urls_counter += 1 if domain_is_in_blacklist(url): print('[DOMAIN BLACKLIST]: {}'.format(url), flush=True) domain_blacklist_counter += 1 elif extention_is_in_blacklist(url): print('[EXTENTION BLACKLIST]: {}'.format(url), flush=True) extention_blacklist_counter += 1 elif len(url) <= 8: print('[SHORT URL]: {}'.format(url), flush=True) short_url_counter += 1 elif url_is_malformed(url): print('[MALFORMED URL]: {}'.format(url), flush=True) malformed_url_counter += 1 elif url in urls: print('[DUPLICATE URL]: {}'.format(url), flush=True) duplicate_url_counter += 1 else: urls.add(url) if urls_counter % 100000 == 0: print_progress('PROGRESS', start_time, urls_counter, domain_blacklist_counter, extention_blacklist_counter, short_url_counter, malformed_url_counter, duplicate_url_counter) print_progress('FINAL', start_time, urls_counter, domain_blacklist_counter, extention_blacklist_counter, short_url_counter, malformed_url_counter, duplicate_url_counter) # Write the final set of urls. print('> writing cleaned up url list to {}'.format(output)) with open(output, 'w') as f: for url in urls: f.write(url + '\n') print('done :-)') pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tools/openwebtext/cleanup_dataset.py000066400000000000000000000102171443434453700311540ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import ftfy import json from langdetect import detect import numpy as np import time import os import sys from tokenizer import Tokenizer MIN_DOCUMENT_LENGHT = 128 def print_progress(prefix, start_time, num_docs, num_fixed_text, num_non_english_docs, chars_non_english_docs, num_small_docs, chars_small_docs): string = prefix + ' | ' string += 'elapsed time: {:.2f} | '.format(time.time() - start_time) string += 'documents: {} | '.format(num_docs) string += 'fixed text: {} | '.format(num_fixed_text) string += 'non-english: {} | '.format(num_non_english_docs) string += 'non-english chars: {} | '.format(chars_non_english_docs) string += 'small docs: {} | '.format(num_small_docs) string += 'small docs chars: {}'.format(chars_small_docs) print(string, flush=True) def filter_corpus(filename, out_filename, print_interval=10000): print(' > filtering {}'.format(filename)) tokenizer = Tokenizer(cache_dir='./cache') num_docs = 0 num_written_docs = 0 num_small_docs = 0 num_fixed_text = 0 num_non_english_docs = 0 chars_non_english_docs = 0 chars_small_docs = 0 start_time = time.time() with open(out_filename, 'wb') as f: with open(filename, 'r') as fin: for line in fin: try: num_docs += 1 myjson = json.loads(line) # Fix text text = ftfy.fix_text(myjson['text']) if text != myjson['text']: num_fixed_text += 1 myjson['text'] = text # Detect language. if detect(text) != 'en': print('[non-english text]', myjson) num_non_english_docs += 1 chars_non_english_docs += len(text) continue # On average each token is 5 characters so 8 is an # upper bound. if len(text) < (8 * MIN_DOCUMENT_LENGHT): tokens = tokenizer.tokenize_document(text) if len(tokens) < MIN_DOCUMENT_LENGHT: print('[small document, skipping]:', myjson) num_small_docs += 1 chars_small_docs += len(text) continue myjson = json.dumps(myjson, ensure_ascii=False) f.write(myjson.encode('utf-8')) f.write('\n'.encode('utf-8')) num_written_docs += 1 if num_docs % print_interval == 0: print_progress('[PROGRESS]', start_time, num_docs, num_fixed_text, num_non_english_docs, chars_non_english_docs, num_small_docs, chars_small_docs) except Exception as e: print(' skipping ', line, e) print_progress('[FINAL]', start_time, num_docs, num_fixed_text, num_non_english_docs, chars_non_english_docs, num_small_docs, chars_small_docs) if __name__ == '__main__': print('building gpt2 dataset ...') input_filename = sys.argv[1] output_filename = sys.argv[2] print('will be reading {}'.format(input_filename)) print('and will write the results to {}'.format(output_filename)) filter_corpus(input_filename, output_filename) pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tools/openwebtext/find_duplicates.py000066400000000000000000000072021443434453700311550ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import itertools import json from lsh import cache, minhash import time import sys # This function is adapted from: # https://github.com/mattilyra/LSH/blob/master/examples/Introduction.ipynb def shingles(text, char_ngram=5): return set(text[head:head + char_ngram] for head in range(0, len(text) - char_ngram)) # This function is adapted from: # https://github.com/mattilyra/LSH/blob/master/examples/Introduction.ipynb def jaccard(set_a, set_b): intersection = set_a & set_b union = set_a | set_b return len(intersection) / len(union) if __name__ == '__main__': print('finding possible duplicate content ...') input = sys.argv[1] output = sys.argv[2] hasher = minhash.MinHasher(seeds=100, char_ngram=5, hashbytes=4) lshcache = cache.Cache(bands=10, hasher=hasher) counter = 0 url_doc = {} start_time = time.time() with open(input, 'r') as f: for line in f: try: myjson = json.loads(line) url = myjson['url'] text = myjson['text'] counter += 1 url_doc[url] = text lshcache.add_fingerprint(hasher.fingerprint(text), url) except Exception as e: print('Error:', e) if counter % 10000 == 0: print(' [read]> processed {} documents in {:.2f} seconds ...'. format(counter, time.time() - start_time), flush=True) counter = 0 start_time = time.time() deduped = 0 with open(output, 'wb') as f: for b in lshcache.bins: for bucket_id in b: if len(b[bucket_id]) > 1: items = list(b[bucket_id]) main_url = items[0] main_dhingles = shingles(url_doc[main_url]) remove_urls = [] for i in range(1, len(items)): counter += 1 other_url= items[i] other_shingles = shingles(url_doc[other_url]) try: jaccard_sim = jaccard(main_dhingles, other_shingles) except Exception as e: print('Error:', e) if jaccard_sim > 0.5: remove_urls.append({other_url: jaccard_sim}) deduped += 1 if counter % 10000 == 0: print(' [write]> processed {} documents in {:.2f} ' 'seoncds and deduped {} documents ...'. format(counter, time.time() - start_time, deduped), flush=True) if len(remove_urls) > 0: myjson = json.dumps({main_url: remove_urls}, ensure_ascii=False) f.write(myjson.encode('utf-8')) f.write('\n'.encode('utf-8')) print('done :-)') pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tools/openwebtext/group_duplicates_url.py000066400000000000000000000060621443434453700322560ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import time import sys def is_similar(jaccard_similarity): return (js >= 0.9) if __name__ == '__main__': print('grouping duplicate urls ...') input = sys.argv[1] output = sys.argv[2] url_to_index = {} index_to_urls = [] counter = 0 start_time = time.time() with open(input, 'r') as f: for line in f: counter += 1 myjson = json.loads(line) urls = [] for main_url in myjson.keys(): urls.append(main_url) for value in myjson[main_url]: for other_url, js in value.items(): if is_similar(js): urls.append(other_url) current_index = -1 other_indices = set() for url in urls: if url in url_to_index: if current_index == -1: current_index = url_to_index[url] elif current_index != url_to_index[url]: other_indices.add(url_to_index[url]) if current_index == -1: current_index = len(index_to_urls) index_to_urls.append(set()) for url in urls: url_to_index[url] = current_index index_to_urls[current_index].add(url) for index in other_indices: for url in index_to_urls[index]: index_to_urls[current_index].add(url) url_to_index[url] = current_index index_to_urls[index] = None if counter % 100000 == 0: print(' > processed {} lines in {} seconds ...'.format( counter, time.time() - start_time)) total_remove = 0 total_remain = 0 for urls in index_to_urls: if urls is not None: if len(urls) > 1: total_remove += (len(urls) - 1) total_remain += 1 print('out of {} urls, only {} are unique and {} should be removed'.format( total_remove+total_remain, total_remain, total_remove)) with open(output, 'wb') as f: for i, urls in enumerate(index_to_urls): if urls is not None: if len(urls) > 1: myjson = json.dumps({str(i): list(urls)}, ensure_ascii=False) f.write(myjson.encode('utf-8')) f.write('\n'.encode('utf-8')) pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tools/openwebtext/merge_jsons.py000066400000000000000000000030561443434453700303360ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import glob import sys import json import argparse if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument("--json_path", type=str, default=".", help="path where all the json files are located") parser.add_argument("--output_file", type=str, default="merged_output.json", help="filename where the merged json should go") args = parser.parse_args() json_path = args.json_path out_file = args.output_file json_files = glob.glob(json_path + '/*.json') counter = 0 with open(out_file, 'w') as outfile: for fname in json_files: counter += 1 if counter % 1024 == 0: print("Merging at ", counter, flush=True) with open(fname, 'r') as infile: for row in infile: each_row = json.loads(row) outfile.write(row) print("Merged file", out_file, flush=True) pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tools/openwebtext/remove_group_duplicates.py000066400000000000000000000047531443434453700327560ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import json import time import sys if __name__ == '__main__': url_filename = sys.argv[1] data_filename = sys.argv[2] output_filename = sys.argv[3] urls = set() with open(url_filename, 'r') as f: for line in f: myjson = json.loads(line) for key in myjson: this_urls = myjson[key] for i in range(1, len(this_urls)): urls.add(this_urls[i]) print('will be removing {} urls'.format(len(urls)), flush=True) written_docs = 0 removed_docs = 0 removed_chars = 0 start_time = time.time() with open(output_filename, 'wb') as fout: with open(data_filename, 'r') as fin: for line in fin: try: myjson = json.loads(line) url = myjson['url'] if url in urls: print('removing', myjson) removed_docs += 1 removed_chars += len(myjson['text']) continue myjson = json.dumps(myjson, ensure_ascii=False) fout.write(myjson.encode('utf-8')) fout.write('\n'.encode('utf-8')) written_docs += 1 if written_docs % 10000 == 0: print(' [PROCESSED] time (s): {:.2f} | written: {} ' '| removed: {} (char: {})'.format( time.time() - start_time, written_docs, removed_docs, removed_chars)) except Exception as e: print('[SKIPPING]', line, e) print(' [PROCESSED] time (s): {:.2f} | written: {} ' '| removed: {} (char: {})'.format( time.time() - start_time, written_docs, removed_docs, removed_chars)) print('done :-)') pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tools/preprocess_data_pangu.py000066400000000000000000000170071443434453700300300ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Processing data for pretraining.""" import os import sys sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir))) import argparse import json import multiprocessing import glob import numpy as np from megatron.tokenizer.tokenization_jieba import JIEBATokenizer import time import torch try: import nltk nltk_available = True except ImportError: nltk_available = False from megatron.data import indexed_dataset # https://stackoverflow.com/questions/33139531/preserve-empty-lines-with-nltks-punkt-tokenizer class CustomLanguageVars(nltk.tokenize.punkt.PunktLanguageVars): _period_context_fmt = r""" \S* # some word material %(SentEndChars)s # a potential sentence ending \s* # <-- THIS is what I changed (?=(?P %(NonWord)s # either other punctuation | (?P\S+) # <-- Normally you would have \s+ here ))""" class IdentitySplitter(object): def tokenize(self, *text): return text class Encoder(object): def __init__(self, args): self.args = args self.tokenizer = JIEBATokenizer(self.args.vocab_file) def encode(self, iterator): key = self.args.json_keys[0] len_paras = 0 ids = {} doc_ids = [] encode_start_time = time.time() file_num = 0 for file_path in iterator: print(file_path) each_start_time = time.time() json_line = open(file_path, 'r', encoding='utf-8') strr = json_line.read() lista = strr.split('\n\n') len_paras += len(lista) for para in lista: if para: contenta = self.tokenizer.tokenize(para) para_ids = self.tokenizer.convert_tokens_to_ids(contenta) if len(para_ids) > 0: doc_ids.append(para_ids) if self.args.append_eod: for i in range(self.args.eod_num): doc_ids[-1].append(self.tokenizer.eod_id) # print(doc_ids) each_end_time = time.time() print("encode this file using {}s".format(each_end_time - each_start_time)) ids[key] = doc_ids encode_end_time = time.time() print("FINISHING ENCODING, USING {}s".format(encode_end_time - encode_start_time)) return ids, len_paras # print('len_paras',len_paras) def package_file(it, n): """ package multiple files""" stop = False while not stop: batch = [] for _ in range(n): try: batch.append(next(it)) except StopIteration: stop = True if not batch: break yield batch def get_args(): parser = argparse.ArgumentParser() group = parser.add_argument_group(title='input data') group.add_argument('--input', type=str, default='/raid/gpt3-train-data/data-v1/new2016zh/txt-data/train/0000*.txt', help='Path to input txt') group.add_argument('--json-keys', nargs='+', default=['text'], help='space separate listed of keys to extract from json') group.add_argument('--split-sentences', action='store_true', help='Split documents into sentences.') group = parser.add_argument_group(title='tokenizer') group.add_argument('--vocab-file', type=str, default='megatron/tokenizer/bpe_4w_pcl/vocab', help='Path to the vocab file') group.add_argument('--append-eod', action='store_true', help='Append an token to the end of a document.') group.add_argument('--eod-num', type=int, default=1, help='eot number.') group = parser.add_argument_group(title='output data') group.add_argument('--output-prefix', type=str, required=True, help='Path to binary output file without suffix') group.add_argument('--dataset-impl', type=str, default='mmap', choices=['lazy', 'cached', 'mmap']) group = parser.add_argument_group(title='runtime') group.add_argument('--workers', type=int, default=200, help='Number of worker processes to launch') group.add_argument('--log-interval', type=int, default=1, help='Interval between progress updates') args = parser.parse_args() args.keep_empty = False return args def main(): args = get_args() startup_start = time.time() print("Opening", args.input) file_iter = glob.iglob(args.input) if nltk_available and args.split_sentences: nltk.download("punkt", quiet=True) encoder = Encoder(args) # tokenizer = JIEBATokenizer(vocab_path, tokenizer_path) pool = multiprocessing.Pool(args.workers) encoded_docs = pool.imap(encoder.encode, package_file(file_iter, 128))#, all_lens)) #encoded_docs = map(encoder.encode, fin) print('encoded_docs',encoded_docs) level = "document" if args.split_sentences: level = "sentence" # print(f"Vocab size: {tokenizer.vocab_size}") print(f"Output prefix: {args.output_prefix}") output_bin_files = {} output_idx_files = {} builders = {} for key in args.json_keys: output_bin_files[key] = "{}{}_{}.bin".format(args.output_prefix, key, level) output_idx_files[key] = "{}{}_{}.idx".format(args.output_prefix, key, level) builders[key] = indexed_dataset.make_builder(output_bin_files[key], impl=args.dataset_impl, vocab_size=encoder.tokenizer.vocab_size) startup_end = time.time() proc_start = time.time() total_bytes_processed = 0 print("Time to startup:", startup_end - startup_start) for i, (doc, bytes_processed) in enumerate(encoded_docs, start=1): total_bytes_processed += bytes_processed for key, sentences in doc.items(): for sentence in sentences: builders[key].add_item(torch.IntTensor(sentence)) builders[key].end_document() if i % args.log_interval == 0: current = time.time() elapsed = current - proc_start mbs = total_bytes_processed/elapsed/1024/1024 print(f"Processed {i} documents", f"({i/elapsed} docs/s, {mbs} MB/s).", file=sys.stderr) for key in args.json_keys: builders[key].finalize(output_idx_files[key]) end_time = time.time() print('Preprocess data using {}s'.format(end_time - startup_end)) if __name__ == '__main__': main() pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tools/splitMergedCkpt_v0.py000066400000000000000000000220631443434453700271640ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Merge model parallel partitions.""" import os import sys from megatron.initialize import initialize_megatron import copy sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir))) import numpy as np import torch from megatron import mpu from megatron.checkpointing import ensure_directory_exists from megatron.checkpointing import get_checkpoint_name from megatron.checkpointing import get_checkpoint_tracker_filename from megatron.global_vars import rebuild_tokenizer from megatron.global_vars import _parse_args # os.environ["CUDA_VISIBLE_DEVICES"] = "4,5,6,7" # os.system("export CUDA_VISIBLE_DEVICES=4,5,6,7") ######################### # --model-type GPT2 --model-parallel-size 2 --tokenizer-type GPT2BPETokenizer --vocab-file /ghome/yands/pclproject/gpt/Megatron-LM-1.1/megatron/tokenizer/bpe_4w_pcl/vocab --num-layers 24 --hidden-size 1024 --num-attention-heads 16 --seq-length 1024 --max-position-embeddings 1024 --load /ghome/yands/model/checkPoints/megatron-1.1-pangu # --model-type GPT2 --model-parallel-size 4 --tokenizer-type GPT2BPETokenizer --vocab-file /ghome/yands/pclproject/gpt/Megatron-LM-1.1/megatron/tokenizer/bpe_4w_pcl/vocab --num-layers 31 --hidden-size 2560 --num-attention-heads 32 --seq-length 1024 --max-position-embeddings 1024 --load /ghome/yands/model/checkPoints/megatron-1.1-pangu-2.6B ######################### def ensure_directory_exists(filename): """Build filename's path if it does not already exists.""" dirname = os.path.dirname(filename) if not os.path.exists(dirname): os.makedirs(dirname) def split_into_partitions(tensor, num_partitions, partition_dim): per_partition_size = mpu.utils.divide(tensor.size(partition_dim), num_partitions) partitions_list = torch.split(tensor, per_partition_size, dim=partition_dim) return partitions_list def get_parallel_checkpoint_name(path): tracker_filename = get_checkpoint_tracker_filename(path) iteration = 0 with open(tracker_filename, 'r') as f: metastring = f.read().strip() iteration = int(metastring) assert iteration > 0 checkpoint_name = get_checkpoint_name(path, iteration) return checkpoint_name, iteration def splitTensorlIntoPartition(tensor, tensorNewList, mpPartitions, tensorName, dim): tensor = tensor[tensorName] partitionsList = split_into_partitions(tensor, mpPartitions, dim) for i, partition in enumerate(partitionsList): tensorNewList[i][tensorName] = partition def loadPartionEmbending(model, modelNewList, mpPartitions): embedding = model['model']['language_model']['embedding']['word_embeddings'] embeddingNewList = [modelNew['model']['language_model']['embedding']['word_embeddings'] for modelNew in modelNewList] tensorName = f'weight' if mpPartitions == 2: embeddingSizePerPartition = 20096 a = embedding[tensorName] newSize = embeddingSizePerPartition*mpPartitions b = torch.rand(newSize-a.shape[0], a.shape[1]) embedding[tensorName] = torch.cat((a,b),0) splitTensorlIntoPartition(embedding, embeddingNewList, mpPartitions, tensorName, 0) def loadPartionAttentionLayer(model, modelNewList, mpPartitions,layerID): transformer = model['model']['language_model']['transformer'] transformerNewList = [modelNew['model']['language_model']['transformer'] for modelNew in modelNewList] tensorName = f'layers.{layerID}.attention.query.weight' splitTensorlIntoPartition(transformer, transformerNewList, mpPartitions, tensorName, 0) tensorName = f'layers.{layerID}.attention.query.bias' splitTensorlIntoPartition(transformer, transformerNewList, mpPartitions, tensorName, 0) tensorName = f'layers.{layerID}.attention.key.weight' splitTensorlIntoPartition(transformer, transformerNewList, mpPartitions, tensorName, 0) tensorName = f'layers.{layerID}.attention.key.bias' splitTensorlIntoPartition(transformer, transformerNewList, mpPartitions, tensorName, 0) tensorName = f'layers.{layerID}.attention.value.weight' splitTensorlIntoPartition(transformer, transformerNewList, mpPartitions, tensorName, 0) tensorName = f'layers.{layerID}.attention.value.bias' splitTensorlIntoPartition(transformer, transformerNewList, mpPartitions, tensorName, 0) tensorName = f'layers.{layerID}.attention.dense.weight' splitTensorlIntoPartition(transformer, transformerNewList, mpPartitions, tensorName, 1) # tensorName = f'layers.{layerID}.attention.dense.bias' # splitTensorlIntoPartition(transformer, transformerNewList, mpPartitions, tensorName, 0) tensorName = f'layers.{layerID}.mlp.dense_h_to_4h.weight' splitTensorlIntoPartition(transformer, transformerNewList, mpPartitions, tensorName, 0) tensorName = f'layers.{layerID}.mlp.dense_h_to_4h.bias' splitTensorlIntoPartition(transformer, transformerNewList, mpPartitions, tensorName, 0) tensorName = f'layers.{layerID}.mlp.dense_4h_to_h.weight' splitTensorlIntoPartition(transformer, transformerNewList, mpPartitions, tensorName, 1) # tensorName = f'layers.{layerID}.mlp.dense_4h_to_h.bias' # splitTensorlIntoPartition(transformer, transformerNewList, mpPartitions, tensorName, 0) def loadPartionTopQueryLayer(model, modelNewList, mpPartitions): transformer = model['model']['language_model']['transformer'] transformerNewList = [modelNew['model']['language_model']['transformer'] for modelNew in modelNewList] tensorName = f'topQueryLayer.attention.query.weight' splitTensorlIntoPartition(transformer, transformerNewList, mpPartitions, tensorName, 0) tensorName = f'topQueryLayer.attention.query.bias' splitTensorlIntoPartition(transformer, transformerNewList, mpPartitions, tensorName, 0) tensorName = f'topQueryLayer.attention.key.weight' splitTensorlIntoPartition(transformer, transformerNewList, mpPartitions, tensorName, 0) tensorName = f'topQueryLayer.attention.key.bias' splitTensorlIntoPartition(transformer, transformerNewList, mpPartitions, tensorName, 0) tensorName = f'topQueryLayer.attention.value.weight' splitTensorlIntoPartition(transformer, transformerNewList, mpPartitions, tensorName, 0) tensorName = f'topQueryLayer.attention.value.bias' splitTensorlIntoPartition(transformer, transformerNewList, mpPartitions, tensorName, 0) tensorName = f'topQueryLayer.attention.dense.weight' splitTensorlIntoPartition(transformer, transformerNewList, mpPartitions, tensorName, 1) # tensorName = f'layers.{layerID}.attention.dense.bias' # splitTensorlIntoPartition(transformer, transformerNewList, mpPartitions, tensorName, 0) tensorName = f'topQueryLayer.mlp.dense_h_to_4h.weight' splitTensorlIntoPartition(transformer, transformerNewList, mpPartitions, tensorName, 0) tensorName = f'topQueryLayer.mlp.dense_h_to_4h.bias' splitTensorlIntoPartition(transformer, transformerNewList, mpPartitions, tensorName, 0) tensorName = f'topQueryLayer.mlp.dense_4h_to_h.weight' splitTensorlIntoPartition(transformer, transformerNewList, mpPartitions, tensorName, 1) # tensorName = f'layers.{layerID}.mlp.dense_4h_to_h.bias' # splitTensorlIntoPartition(transformer, transformerNewList, mpPartitions, tensorName, 0) def main(): mpPartitions = 2 numLayers = 31 partitionId = 0 ckptName = '/userhome/model/checkPoints/megatron-1.1-pangu-2.6B/merged_v2/iter_0007800/mp_rank_00/model_optim_rng.pt' model = torch.load(ckptName) transformerNewList = [] modelNewList = [copy.deepcopy(model) for i in range(mpPartitions)] loadPartionEmbending(model, modelNewList, mpPartitions) for layerID in range(numLayers): loadPartionAttentionLayer(model, modelNewList, mpPartitions, layerID) loadPartionTopQueryLayer(model, modelNewList, mpPartitions) save = '/userhome/model/checkPoints/megatron-1.1-pangu-2.6B/merged_split' iteration = 'iter_0007800' modelName = 'model_optim_rng.pt' for rank, model_ in enumerate(modelNewList): rankName = 'mp_rank_{:02d}'.format(rank) savePath='/'.join([save,iteration,rankName,modelName]) ensure_directory_exists(savePath) tensorName = 'layers.0.attention.query.weight' print(model_['model']['language_model']['transformer'][tensorName].shape) torch.save(model_, savePath) pass if __name__ == '__main__': main() pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/tools/split_full_model_into_mp_model.py000066400000000000000000000204241443434453700317170ustar00rootroot00000000000000import os import sys sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir))) import torch from megatron import mpu from megatron.checkpointing import ensure_directory_exists from megatron.checkpointing import get_checkpoint_name from megatron.checkpointing import get_checkpoint_tracker_filename from megatron.global_vars import rebuild_tokenizer from megatron.global_vars import _parse_args def get_model(model_type): if model_type == 'BERT': from pretrain_bert import model_provider elif model_type == 'GPT2': from pretrain_gpt2 import model_provider elif model_type == 'Pangu': from pretrain_gpt2 import model_provider elif model_type == 'RACE': from tasks.race.finetune import model_provider elif model_type == ['MNLI', 'QQP']: num_classes = 2 if model_type == 'MNLI': num_classes = 3 from megatron.model.classification import Classification def model_provider(): return Classification(num_classes=num_classes, num_tokentypes=2) else: raise Exception('unrecognized model type: {}'.format(model_type)) model = model_provider() model = model.half() return model def get_mp_split_args(parser): """Provide extra arguments required for merging.""" group = parser.add_argument_group(title='mp merge') group.add_argument('--model-type', type=str, required=True, choices=['BERT', 'GPT2', 'Pangu', 'RACE', 'MNLI', 'QQP'], help='Type of the mdoel.') group.add_argument('--num-mp-model', type=int, required=True, help='Number of mp model you want to divid into.') group.add_argument('--mp-model-save', type=str, required=True, help='Dir of saving model parallel model.') return parser def get_full_model_loadCkpt(args): args.model_parallel_size = 1 tokenizer = rebuild_tokenizer(args) mpu.initialize.set_model_parallel_world_size(1) mpu.initialize.set_model_parallel_rank(0) model = get_model(args.model_type) tracker_filename = get_checkpoint_tracker_filename(args.load) with open(tracker_filename, 'r') as f: metastring = f.read().strip() iteration = int(metastring) checkpoint_name = get_checkpoint_name(args.load, iteration) sd = torch.load(checkpoint_name, map_location='cpu') model.load_state_dict(sd['model']) return model def get_one_partition_model(args, rank): args.model_parallel_size = args.num_mp_model tokenizer = rebuild_tokenizer(args) mpu.initialize.set_model_parallel_world_size(args.num_mp_model) mpu.initialize.set_model_parallel_rank(rank) model = get_model(args.model_type) return model def split_into_partitions(tensor, num_partitions, partition_dim, stride, rank): per_partition_size = mpu.utils.divide(tensor.size(partition_dim), num_partitions) per_partition_per_stride_size = mpu.utils.divide(per_partition_size, stride) partitions_list = torch.split(tensor, per_partition_per_stride_size, dim=partition_dim) partitions = [] for i in range(num_partitions): partition = torch.cat(partitions_list[i::num_partitions], dim=partition_dim) partitions.append(partition) return partitions[rank] def split_full_model(full_parameter, partition_parameter, partition_dim, stride, rank, num_partitions): per_partition_size = partition_parameter.size(partition_dim) def split(): with torch.no_grad(): if (per_partition_size * num_partitions) == full_parameter.size( partition_dim): partition_parameter.data.copy_( split_into_partitions( full_parameter, num_partitions, partition_dim, stride, rank ).data ) else: assert full_parameter.size(partition_dim) \ <= per_partition_size * num_partitions dim_diff = (per_partition_size * num_partitions) - \ full_parameter.size(partition_dim) print(' ***WARNING*** sizes do not match. Will add ' 'the merged partitions by {} along dimension {} ' 'to increase the size from {} to {} ...'.format( dim_diff, partition_dim, full_parameter.size(partition_dim), per_partition_size * num_partitions) ) size = list(full_parameter.size()) size[partition_dim] = dim_diff add_tensor = torch.ones(size) full_parameter_ = torch.cat((full_parameter.data, add_tensor), partition_dim) assert full_parameter_.size(partition_dim) \ == per_partition_size * num_partitions partition_parameter.data.copy_( split_into_partitions( full_parameter_, num_partitions, partition_dim, stride, rank ).data ) # If stride is 1, then do simple concatination. if stride == 1: split() return else : raise ValueError("stride must be 1") return def load_one_partition_model(partition_model, full_model, rank): full_params_gen = full_model.named_parameters() partition_params_gen = partition_model.named_parameters() while True: try: # Get the params and check names. name, full_param = next(full_params_gen) partition_name, partition_param = next(partition_params_gen) print(' > working on {} ...'.format(name)) print(' merged type: {}, size: {}'.format( full_param.dtype, list(full_param.size()))) assert partition_name == name print(' partition {} type: {}, size: {}'.format( rank, partition_param.dtype, list(partition_param.size()))) # For the non-parallel parameters, simply copy from full model. if not hasattr(full_param, 'model_parallel'): print(' none-parallel parameter, simple copy from full model') with torch.no_grad(): partition_param.data.copy_(full_param.data) # For parallel parameters, split full model else: print(' split full model with stride {} along ' 'dimention {}'.format(full_param.stride, full_param.partition_dim)) split_full_model(full_param, partition_param, full_param.partition_dim, full_param.stride, rank, args.num_mp_model) # split_to_partitions(full_param, # partitions_param, # full_param.partition_dim, # full_param.stride) except StopIteration: break if __name__ == '__main__': args = _parse_args(extra_args_provider=get_mp_split_args) save = args.mp_model_save iteration = 'iter_0007800' # inter_*** any id is ok modelName = 'model_optim_rng.pt' full_model = get_full_model_loadCkpt(args) for rank in range(args.num_mp_model): partition_model = get_one_partition_model(args, rank) load_one_partition_model(partition_model, full_model, rank) sd = {} sd['model'] = partition_model.state_dict_for_save_checkpoint() sd['iteration'] = iteration rankName = 'mp_rank_{:02d}'.format(rank) savePath='/'.join([save,iteration,rankName,modelName]) ensure_directory_exists(savePath) torch.save(sd, savePath) recordFile = open(get_checkpoint_tracker_filename(save), 'w') recordFile.writelines(iteration.split('_')[1]) recordFile.close() print('Done!') print('Checkpoints saved to {}'.format(save)) pass pcl_pangu/pcl_pangu/model_converter/000077500000000000000000000000001443434453700202005ustar00rootroot00000000000000pcl_pangu/pcl_pangu/model_converter/__init__.py000066400000000000000000000003301443434453700223050ustar00rootroot00000000000000from .ms_2_numpy import ms_2_numpy from .numpy_2_pt import numpy_2_pt from .split_pytorch_model import split_pt from .merge_pytorch_model import merge_pt from pcl_pangu.onnx_inference.conver_pt_onnx import pt_2_onnx8pcl_pangu/pcl_pangu/model_converter/_numpy_ckpt_2_pytorch.py000066400000000000000000000355261443434453700251060ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Merge model parallel partitions.""" import os import numpy as np def get_model(model_type): if model_type == 'BERT': from pretrain_bert import model_provider elif model_type == 'GPT2': from pretrain_gpt2 import model_provider elif model_type == 'Pangu': from pretrain_gpt2 import model_provider elif model_type == 'RACE': from tasks.race.finetune import model_provider elif model_type == ['MNLI', 'QQP']: num_classes = 2 if model_type == 'MNLI': num_classes = 3 from megatron.model.classification import Classification def model_provider(): return Classification(num_classes=num_classes, num_tokentypes=2) else: raise Exception('unrecognized model type: {}'.format(model_type)) model = model_provider() model = model.half() return model def get_change_ckpt_args(parser): """Provide extra arguments required for merging.""" group = parser.add_argument_group(title='mp merge') group.add_argument('--model-type', type=str, required=True, choices=['BERT', 'GPT2', 'Pangu', 'RACE', 'MNLI', 'QQP'], help='Type of the mdoel.') group.add_argument('--npy-ckpt-path', type=str, required=True, help='path of npy checkpoint.') return parser def loadModelFromNp(sd, args): import torch num_layers = args.num_layers npCkptPath = args.npy_ckpt_path languageModel = sd['model']['language_model'] loadEmbeddingFromNp(npCkptPath, languageModel) transformer = sd['model']['language_model']['transformer'] for layerID in range(num_layers): loadAttentionLayerFromNp(npCkptPath, transformer, layerID) loadQueryLayerFromNp(npCkptPath, transformer) transformer['final_layernorm.weight'][:] = \ torch.tensor( np.load(npCkptPath+f'backbone.layernorm.gamma.npy') ).float() transformer['final_layernorm.bias'][:] = \ torch.tensor( np.load(npCkptPath+f'backbone.layernorm.beta.npy') ).float() def loadEmbeddingFromNp(npCkptPath, languageModel, vocabSize=40000): import torch word_embedding_np = \ np.load(npCkptPath+'backbone.word_embedding.embedding_table.npy') languageModel['embedding']['word_embeddings']['weight'][:vocabSize,:] = \ torch.tensor(word_embedding_np).float() position_embeddings_np = \ np.load(npCkptPath+'backbone.position_embedding.embedding_table.npy') languageModel['embedding']['position_embeddings']['weight'][:,:] = \ torch.tensor(position_embeddings_np).float() topQueryEmbedding_np = \ np.load(npCkptPath+'backbone.top_query_embedding.embedding_table.npy') languageModel['topQueryEmbedding']['top_query_embeddings']['weight'][:,:] = \ torch.tensor(topQueryEmbedding_np).float() def loadAttentionLayerFromNp(npCkptPath, transformer, layerID): import torch attention_dense1_weight_np = \ np.load(npCkptPath+f'backbone.blocks.{layerID}.attention.dense1.weight.npy') attention_dense2_weight_np = \ np.load(npCkptPath+f'backbone.blocks.{layerID}.attention.dense2.weight.npy') attention_dense3_weight_np = \ np.load(npCkptPath+f'backbone.blocks.{layerID}.attention.dense3.weight.npy') attention_dense1_bias_np = \ np.load(npCkptPath+f'backbone.blocks.{layerID}.attention.dense1.bias.npy') attention_dense2_bias_np = \ np.load(npCkptPath+f'backbone.blocks.{layerID}.attention.dense2.bias.npy') attention_dense3_bias_np = \ np.load(npCkptPath+f'backbone.blocks.{layerID}.attention.dense3.bias.npy') query_weight = transformer[f'layers.{layerID}.attention.query.weight'] key_weight = transformer[f'layers.{layerID}.attention.key.weight'] value_weight = transformer[f'layers.{layerID}.attention.value.weight'] query_weight[:] = torch.tensor(attention_dense1_weight_np).float() key_weight[:] = torch.tensor(attention_dense2_weight_np).float() value_weight[:] = torch.tensor(attention_dense3_weight_np).float() query_bias = transformer[f'layers.{layerID}.attention.query.bias'] key_bias = transformer[f'layers.{layerID}.attention.key.bias'] value_bias = transformer[f'layers.{layerID}.attention.value.bias'] query_bias[:] = torch.tensor(attention_dense1_bias_np).float() key_bias[:] = torch.tensor(attention_dense2_bias_np).float() value_bias[:] = torch.tensor(attention_dense3_bias_np).float() att_dense_weight = transformer[f'layers.{layerID}.attention.dense.weight'] att_dense_weight[:,:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.attention.projection.weight.npy').transpose() ).float() att_dense_bias = transformer[f'layers.{layerID}.attention.dense.bias'] att_dense_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.attention.projection.bias.npy') ).float() mlp_dense_h_to_4h_weight = transformer[f'layers.{layerID}.mlp.dense_h_to_4h.weight'] mlp_dense_h_to_4h_weight[:,:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.output.mapping.weight.npy').transpose() ).float() mlp_dense_h_to_4h_bias = transformer[f'layers.{layerID}.mlp.dense_h_to_4h.bias'] mlp_dense_h_to_4h_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.output.mapping.bias.npy') ).float() mlp_dense_4h_to_h_weight = transformer[f'layers.{layerID}.mlp.dense_4h_to_h.weight'] mlp_dense_4h_to_h_weight[:,:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.output.projection.weight.npy').transpose() ).float() mlp_dense_4h_to_h_bias = transformer[f'layers.{layerID}.mlp.dense_4h_to_h.bias'] mlp_dense_4h_to_h_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.output.projection.bias.npy') ).float() input_layernorm_weight = transformer[f'layers.{layerID}.input_layernorm.weight'] input_layernorm_weight[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.layernorm1.gamma.npy') ).float() input_layernorm_bias = transformer[f'layers.{layerID}.input_layernorm.bias'] input_layernorm_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.layernorm1.beta.npy') ).float() post_attention_layernorm_weight = transformer[f'layers.{layerID}.post_attention_layernorm.weight'] post_attention_layernorm_weight[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.layernorm2.gamma.npy') ).float() post_attention_layernorm_bias = transformer[f'layers.{layerID}.post_attention_layernorm.bias'] post_attention_layernorm_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.layernorm2.beta.npy') ).float() input_layernorm_weight = transformer[f'layers.{layerID}.input_layernorm.weight'] input_layernorm_weight[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.layernorm1.gamma.npy') ).float() input_layernorm_bias = transformer[f'layers.{layerID}.input_layernorm.bias'] input_layernorm_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.layernorm1.beta.npy') ).float() post_attention_layernorm_weight = transformer[f'layers.{layerID}.post_attention_layernorm.weight'] post_attention_layernorm_weight[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.layernorm2.gamma.npy') ).float() post_attention_layernorm_bias = transformer[f'layers.{layerID}.post_attention_layernorm.bias'] post_attention_layernorm_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.blocks.{layerID}.layernorm2.beta.npy') ).float() def loadQueryLayerFromNp(npCkptPath, transformer): import torch attention_dense1_weight_np = \ np.load(npCkptPath+f'backbone.top_query_layer.attention.dense1.weight.npy') attention_dense1_bias_np = \ np.load(npCkptPath+f'backbone.top_query_layer.attention.dense1.bias.npy') attention_dense2_weight_np = \ np.load(npCkptPath+f'backbone.top_query_layer.attention.dense2.weight.npy') attention_dense2_bias_np = \ np.load(npCkptPath+f'backbone.top_query_layer.attention.dense2.bias.npy') attention_dense3_weight_np = \ np.load(npCkptPath+f'backbone.top_query_layer.attention.dense3.weight.npy') attention_dense3_bias_np = \ np.load(npCkptPath+f'backbone.top_query_layer.attention.dense3.bias.npy') query_weight = transformer[f'topQueryLayer.attention.query.weight'] query_weight[:, :] = \ torch.tensor(attention_dense1_weight_np).float() query_bias = transformer[f'topQueryLayer.attention.query.bias'] query_bias[:] = torch.tensor(attention_dense1_bias_np).float() key_weight = transformer[f'topQueryLayer.attention.key.weight'] key_weight[:, :] = \ torch.tensor(attention_dense2_weight_np).float() key_bias = transformer[f'topQueryLayer.attention.key.bias'] key_bias[:] = torch.tensor(attention_dense2_bias_np).float() value_weight = transformer[f'topQueryLayer.attention.value.weight'] value_weight[:, :] = \ torch.tensor(attention_dense3_weight_np).float() value_bias = transformer[f'topQueryLayer.attention.value.bias'] value_bias[:] = torch.tensor(attention_dense3_bias_np).float() att_dense_weight = transformer[f'topQueryLayer.attention.dense.weight'] att_dense_weight[:,:] = \ torch.tensor( np.load(npCkptPath+f'backbone.top_query_layer.attention.projection.weight.npy') .transpose() ).float() att_dense_bias = transformer[f'topQueryLayer.attention.dense.bias'] att_dense_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.top_query_layer.attention.projection.bias.npy') ).float() mlp_dense_h_to_4h_weight = transformer[f'topQueryLayer.mlp.dense_h_to_4h.weight'] mlp_dense_h_to_4h_weight[:,:] = \ torch.tensor( np.load(npCkptPath+f'backbone.top_query_layer.output.mapping.weight.npy') .transpose() ).float() mlp_dense_h_to_4h_bias = transformer[f'topQueryLayer.mlp.dense_h_to_4h.bias'] mlp_dense_h_to_4h_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.top_query_layer.output.mapping.bias.npy') ).float() mlp_dense_4h_to_h_weight = transformer[f'topQueryLayer.mlp.dense_4h_to_h.weight'] mlp_dense_4h_to_h_weight[:,:] = \ torch.tensor( np.load(npCkptPath+f'backbone.top_query_layer.output.projection.weight.npy') .transpose() ).float() mlp_dense_4h_to_h_bias = transformer[f'topQueryLayer.mlp.dense_4h_to_h.bias'] mlp_dense_4h_to_h_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.top_query_layer.output.projection.bias.npy') ).float() input_layernorm_weight = transformer[f'topQueryLayer.input_layernorm.weight'] input_layernorm_weight[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.top_query_layer.layernorm1.gamma.npy') ).float() input_layernorm_bias = transformer[f'topQueryLayer.input_layernorm.bias'] input_layernorm_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.top_query_layer.layernorm1.beta.npy') ).float() post_attention_layernorm_weight = transformer[f'topQueryLayer.post_attention_layernorm.weight'] post_attention_layernorm_weight[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.top_query_layer.layernorm2.gamma.npy') ).float() post_attention_layernorm_bias = transformer[f'topQueryLayer.post_attention_layernorm.bias'] post_attention_layernorm_bias[:] = \ torch.tensor( np.load(npCkptPath+f'backbone.top_query_layer.layernorm2.beta.npy') ).float() def main(): import torch from megatron import mpu from megatron.checkpointing import ensure_directory_exists from megatron.checkpointing import get_checkpoint_name from megatron.checkpointing import get_checkpoint_tracker_filename from megatron.global_vars import rebuild_tokenizer from megatron.global_vars import _parse_args # test_split_merge() # Args args = _parse_args(extra_args_provider=get_change_ckpt_args) model_type = args.model_type # orig_model_parallel_size = args.model_parallel_size args.model_parallel_size = 1 tokenizer = rebuild_tokenizer(args) print('\n merging model parallel partitions ...') print(' > checkpoint path: {}'.format(args.load)) print(' > model parameters:') print(' number of tokens ................ {} '.format( tokenizer.vocab_size)) print(' number of layers ................ {}'.format(args.num_layers)) print(' hidden sise ..................... {}'.format(args.hidden_size)) print(' number of attention heads ....... {}'.format( args.num_attention_heads)) print(' maximum position embeddings ..... {}'.format( args.max_position_embeddings)) # Full model. print('> building the full model ...') mpu.initialize.set_model_parallel_world_size(1) mpu.initialize.set_model_parallel_rank(0) # initialize_megatron(args_defaults={'tokenizer_type': 'GPT2BPETokenizer'}) merged_model = get_model(model_type) iteration = 1000 #any num is ok # Save the model. args.model_parallel_size = 1 mpu.initialize.set_model_parallel_rank(0) sd = {} sd['model'] = merged_model.state_dict_for_save_checkpoint() sd['iteration'] = iteration if not args.npy_ckpt_path.endswith('/'): args.npy_ckpt_path += '/' merged_path = os.path.join(args.npy_ckpt_path, 'merged') checkpoint_name = get_checkpoint_name(merged_path, iteration) ensure_directory_exists(checkpoint_name) loadModelFromNp(sd, args) print('> saving merged model to {}'.format(checkpoint_name)) torch.save(sd, checkpoint_name) recordFile = open(get_checkpoint_tracker_filename(merged_path), 'w') recordFile.writelines(str(iteration)) recordFile.close() print('done :-)') if __name__ == '__main__': main() pcl_pangu/pcl_pangu/model_converter/merge_pytorch_model.py000066400000000000000000000024421443434453700246030ustar00rootroot00000000000000from pcl_pangu.model import alpha from pcl_pangu.context import set_context from pcl_pangu.model.launcher_torch import launch from loguru import logger import os, sys DISTRUBUTED_CONFIG = { 'nnodes':1, 'node_rank':0, 'nproc_per_node':1, 'master_addr':"localhost", 'master_port':29503 } def run_pt(script_args, py_script, **kwargs): current_dir = os.path.dirname(os.path.dirname(__file__)) sys.path.append(current_dir+'/panguAlpha_pytorch') py_script = os.path.join(current_dir, py_script) logger.info("> Running {} with args: {}".format(py_script, script_args)) launch(training_script=py_script, training_script_args=script_args, **DISTRUBUTED_CONFIG, **kwargs) def merge_pt(config, sharding_path, num_ranks): config.save = sharding_path config.load = sharding_path script_args = config._get_training_script_args() script_args.append('--mp-model-parallel-size={}'.format(num_ranks)) script_args.append('--model-type={}'.format('Pangu')) py_script = 'model/panguAlpha_pytorch/tools/merge_mp_partitions.py' run_pt(script_args, py_script) if __name__ == '__main__': merged_path = '/home/yands/tmp/numpy_ckpt_2.6b/2_partition_model' merge_pt(alpha.model_config_gpu(model='2B6'),merged_path,2) passpcl_pangu/pcl_pangu/model_converter/ms_2_numpy.py000066400000000000000000000027341443434453700226500ustar00rootroot00000000000000def ms_2_numpy(ms_ckpt_path, npy_path='./numpy_ckpt/'): from mindspore import save_checkpoint, load_checkpoint, build_searched_strategy, merge_sliced_parameter from mindspore import ops import numpy as np import os from mindspore import context context.set_context(mode=context.PYNATIVE_MODE) # # pwd = '/userhome/model/' # # save = '/userhome/model/panguAlpha_13b_fp16_NumpyCkpt/' # save = '/userhome/model/panguAlpha_2.6b_NumpyCkpt/' # # save = '/userhome/model/tmp/' # # file_name = pwd + 'PanguAlpha_2.6B_fp16.ckpt' # # file_name = pwd + 'PanguAlpha_13b_fp16.ckpt' # file_name = pwd + 'PanguAlpha_2.6B.ckpt' # # file_name = '/Users/sam/Downloads/PanguAlpha_2.6B_fp16.ckpt' param_dict1 = load_checkpoint(ms_ckpt_path) if not os.path.exists(npy_path): os.mkdir(npy_path) keys_same = [] for key in param_dict1.keys(): equal_count = ops.EqualCount() parameter = param_dict1[key] parameter_name = parameter.name parameter_shape = parameter.data.shape parameter_shape_length = len(parameter_shape) npy_path_ = os.path.join(npy_path,parameter_name +'.npy') np.save(npy_path_, parameter.data.asnumpy()) print(type(param_dict1[key])) print(param_dict1[key]) pass if __name__ == '__main__': ms_ckpt_path = '/raid0/yands/tmp/PanguAlpha_2.6B_fp16.ckpt' ms_2_numpy(ms_ckpt_path=ms_ckpt_path, npy_path='/raid0/yands/tmp/numpy_ckpt_2.6b/')pcl_pangu/pcl_pangu/model_converter/numpy_2_pt.py000066400000000000000000000023521443434453700226500ustar00rootroot00000000000000from pcl_pangu.model import alpha from pcl_pangu.context import set_context from pcl_pangu.model.launcher_torch import launch from loguru import logger import os, sys DISTRUBUTED_CONFIG = { 'nnodes':1, 'node_rank':0, 'nproc_per_node':1, 'master_addr':"localhost", 'master_port':29503 } def run_pt(script_args, py_script, **kwargs): current_dir = os.path.dirname(os.path.dirname(__file__)) current_dir = os.path.join(current_dir, 'model_converter') sys.path.append(current_dir+'/panguAlpha_pytorch') py_script = os.path.join(current_dir, py_script) logger.info("> Running {} with args: {}".format(py_script, script_args)) launch(training_script=py_script, training_script_args=script_args, **DISTRUBUTED_CONFIG, **kwargs) def numpy_2_pt(config,numpy_ckpt): script_args = config._get_training_script_args() script_args.append('--npy-ckpt-path={}'.format(numpy_ckpt)) script_args.append('--model-type={}'.format('Pangu')) py_script = 'numpy_ckpt_2_pytorch.py' run_pt(script_args, py_script) if __name__ == '__main__': numpy_ckpt = '/home/yands/tmp/numpy_ckpt_2.6b' numpy_2_pt(alpha.model_config_gpu(model='2B6'), numpy_ckpt) passpcl_pangu/pcl_pangu/model_converter/split_pytorch_model.py000066400000000000000000000031221443434453700246330ustar00rootroot00000000000000from pcl_pangu.model import alpha from pcl_pangu.context import set_context from pcl_pangu.model.launcher_torch import launch from loguru import logger import os, sys DISTRUBUTED_CONFIG = { 'nnodes':1, 'node_rank':0, 'nproc_per_node':1, 'master_addr':"localhost", 'master_port':29503 } def run_pt(script_args, py_script, **kwargs): current_dir = os.path.dirname(os.path.dirname(__file__)) sys.path.append(current_dir+'/panguAlpha_pytorch') py_script = os.path.join(current_dir, py_script) logger.info("> Running {} with args: {}".format(py_script, script_args)) launch(training_script=py_script, training_script_args=script_args, **DISTRUBUTED_CONFIG, **kwargs) def split_pt(config, merged_path, num_ranks): config.save = merged_path config.load = merged_path script_args = config._get_training_script_args() model_save = os.path.join(merged_path, f'{num_ranks}_partition_model') if os.path.exists(model_save): os.system('rm -rf {}'.format(model_save)) os.system('mkdir {}'.format(model_save)) script_args.append('--num-mp-model={}'.format(num_ranks)) script_args.append('--model-type={}'.format('Pangu')) script_args.append('--mp-model-save={}'.format(model_save)) py_script = 'model/panguAlpha_pytorch/tools/split_full_model_into_mp_model.py' run_pt(script_args, py_script) if __name__ == '__main__': from pcl_pangu.model_converter import split_pt merged_path = '/home/yands/tmp/numpy_ckpt_2.6b/merged' split_pt(alpha.model_config_gpu(model='2B6'),merged_path,2) passpcl_pangu/pcl_pangu/online/000077500000000000000000000000001443434453700162755ustar00rootroot00000000000000pcl_pangu/pcl_pangu/online/__init__.py000066400000000000000000000003101443434453700204000ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2022/8/12 # @Author: pcl from .modelinfo.modelinfo import ModelInfo from .infer.infer import Infer __all__ = [ "ModelInfo", "Infer", ] pcl_pangu/pcl_pangu/online/infer/000077500000000000000000000000001443434453700174005ustar00rootroot00000000000000pcl_pangu/pcl_pangu/online/infer/__init__.py000066400000000000000000000001211443434453700215030ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2022/8/12 # @Author: pcl pcl_pangu/pcl_pangu/online/infer/infer.py000066400000000000000000000061161443434453700210610ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2022/8/16 # @Author: pcl import time import requests from pcl_pangu.online.infer.pangu_alpha_dto import reset_default_response, \ send_requests_pangu_alpha, send_requests_pangu_alpha_old, get_response from pcl_pangu.online.infer.pangu_evolution_dto import PanguEvolutionDTO def ErrorMessageConverter(result_response): ErrorMessages = ['Wating for reply TimeoutError', '当前排队人数过多，请稍后再点击！'] WarningMessages = ['OutputEmptyWarning'] if result_response["results"]["generate_text"] in ErrorMessages: result_response["results"]["generate_text"] = '' result_response['status'] = False if result_response["results"]["generate_text"] in WarningMessages: result_response["results"]["generate_text"] = '' result_response['status'] = True return result_response class Infer(object): def __init__(self): pass @classmethod def do_generate_pangu_alpha(cls, model, prompt_input, api_key=None, max_token=None, top_k=None, top_p=None, **kwargs): payload = { 'api_key': api_key, 'u': prompt_input, 'top_k': top_k, 'top_p': top_p, 'result_len': max_token, # 'isWaiting': 'false' } reset_default_response() send_requests_pangu_alpha_old(payload) result_response = get_response() result_response['api_key'] = api_key result_response['model'] = model return ErrorMessageConverter(result_response) @classmethod def do_generate_pangu_evolution(cls, model, prompt_input, api_key=None, max_token=None, top_k=None, top_p=None, **kwargs): request = PanguEvolutionDTO.build_request(model, api_key, prompt_input, max_token, top_k, top_p) default_response = PanguEvolutionDTO.build_default_response(api_key, model, prompt_input) # response = ErrorMessageConverter(PanguEvolutionDTO.do_remote_infer_v1(request, default_response)) response = PanguEvolutionDTO.do_remote_infer_v2(request, default_response) return response @classmethod def generate(cls, model, prompt_input, api_key, max_token=None, top_k=None, top_p=None, **kwargs): """ model: 模型 prompt_input: 文本输入，可以结合prompt做为整体输入 max_token: top_k: 随机采样参数 top_p: 随机采样参数 kwargs: 不同模型支持的其他参数 """ if "pangu-alpha-13B-md"==model: return cls.do_generate_pangu_alpha(model, prompt_input, api_key, max_token, top_k, top_p, **kwargs) elif "pangu-alpha-evolution-2B6-pt"==model: return cls.do_generate_pangu_evolution(model, prompt_input, api_key, max_token, top_k, top_p, **kwargs) elif "chat-pangu"==model: return cls.do_generate_pangu_evolution(model, prompt_input, api_key, max_token, top_k, top_p, **kwargs) else: defalut_response = {"status": "The model does not exist."} print("Error model.") return defalut_response pcl_pangu/pcl_pangu/online/infer/pangu_alpha_dto.py000066400000000000000000000054761443434453700231130ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2022/8/17 # @Author: pcl import requests import time default_response = { "api_key": None, "api_access_status": True, "model": None, "object": "generate", "results": { "prompt_input": None, "generate_text": None, "logprobs": None, }, "status": True } def reset_default_response(): global default_response default_response = { "api_key": None, "api_access_status": True, "model": None, "object": "generate", "results": { "prompt_input": None, "generate_text": None, "logprobs": None, }, "status": True } def auto_payload_topParams(payload): if payload['result_len'] is None: payload['result_len'] = 50 if payload['top_k'] is None and payload['top_p'] is None: payload['top_k'] = 0 payload['top_p'] = 0.9 elif payload['top_k'] is None: payload['top_k'] = 1 elif payload['top_p'] is None: payload['top_p'] = 0.9 return payload def send_requests_pangu_alpha(payload): global default_response payload = auto_payload_topParams(payload) response = requests.get('https://pangu-alpha.pcl.ac.cn/query?', params=payload) if response.status_code == 200: result = response.json()['rsvp'] if result is None: payload['isWaiting'] = 'true' time.sleep(10) send_requests_pangu_alpha(payload) else: default_response['results']['prompt_input'] = payload['u'] default_response['results']['generate_text'] = result[-1] else: default_response['status'] = False print("Error response! checkout your [url] is 'https://pangu-alpha.pcl.ac.cn/query?'\n") def send_requests_pangu_alpha_old(payload): global default_response payload = auto_payload_topParams(payload) response = requests.get('https://pangu-alpha.pcl.ac.cn/query?', params=payload) if response.status_code == 200: result = response.json()['rsvp'] openi_access_flag = response.json()["openi_access_flag"] default_response['results']['prompt_input'] = payload['u'] default_response['results']['generate_text'] = result[-1] if result and openi_access_flag: default_response["results"]["generate_text"] = result[-1] default_response["status"] = True elif openi_access_flag is False: default_response["results"]["generate_text"] = None default_response["status"] = False default_response["api_access_status"] = False else: default_response['status'] = False print("Error response! checkout your [url] is 'https://pangu-alpha.pcl.ac.cn/query?'\n") def get_response(): return default_responsepcl_pangu/pcl_pangu/online/infer/pangu_evolution_dto.py000066400000000000000000000062521443434453700240430ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2022/8/17 # @Author: pcl import time import json import requests class PanguEvolutionDTO(object): pangu_evolution_url_v1 = "https://pangu-alpha.pcl.ac.cn/query_advanced?" pangu_evolution_url_v2 = "https://pangu-alpha.pcl.ac.cn/query_advanced_api" DEFAULT_MAX_TOKEN = 50 TOP_P = 0.0 TOP_K = 1 def __init__(self): pass @classmethod def build_request(cls, model, api_key, prompt_input, max_token, top_k, top_p): max_token = cls.DEFAULT_MAX_TOKEN if max_token is None else max_token top_k = cls.TOP_K if top_k is None else top_k top_p = cls.TOP_P if top_p is None else top_p request = { "api_key": api_key, "input_query": prompt_input, "result_len": max_token, "top_k": top_k, "top_p": top_p, "task_name": "", "model": model, } return request @classmethod def build_default_response(cls, api_key, model, prompt_input): default_response = { "api_key": api_key, "api_access_status": True, "model": model, "object": "generate", "results": { "prompt_input": prompt_input, "generate_text": None, "logprobs": None, }, "status": False } return default_response @classmethod def do_remote_infer_v1(cls, request, default_response): try: response = requests.get(cls.pangu_evolution_url_v1, params=request, headers={'Connection': 'close'}) if response.status_code == 200: result = response.json()["rsvp"] openi_access_flag = response.json()["openi_access_flag"] if result and openi_access_flag: default_response["results"]["generate_text"] = result[-1] default_response["status"] = True return default_response elif openi_access_flag is False: default_response["api_access_status"] = False print("Error api_key!") return default_response except: time.sleep(10) print("Connection refused by the server!") print("Error response!") return default_response @classmethod def do_remote_infer_v2(cls, request, default_response): data = json.dumps(request).encode('utf-8') header = {'Connection': 'close'} try: resp = requests.post(cls.pangu_evolution_url_v2, data=data, headers=header) response = json.loads(resp.text) status_code = response["code"] default_response["results"]["generate_text"] = response["response_info"]["model_response"] default_response["status"] = response["response_info"]["status"] default_response["api_access_status"] = response["response_info"]["api_access_status"] except: time.sleep(5) print("Connection refused by the server!") return default_responsepcl_pangu/pcl_pangu/online/modelinfo/000077500000000000000000000000001443434453700202515ustar00rootroot00000000000000pcl_pangu/pcl_pangu/online/modelinfo/__init__.py000066400000000000000000000001211443434453700223540ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2022/8/12 # @Author: pcl pcl_pangu/pcl_pangu/online/modelinfo/modelinfo.py000066400000000000000000000021431443434453700225770ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2022/8/12 # @Author: pcl from loguru import logger class ModelInfo(object): models = { "data": [ { "id": "pangu-alpha-13B-2", "model": "pangu-alpha-13B-md", "object": "model", "owned_by": "pcl", "permission": ["generate"] }, { "id": "pangu-alpha-evolution-1", "model": "pangu-alpha-evolution-2B6-pt", "object": "model", "owned_by": "pcl", "permission": ["generate"] }, ], "object": "list" } @classmethod def model_list(cls, api_key=None): return cls.models @classmethod def model_info(cls, model, api_key=None): defalut_response = {"status": "The model does not exist."} for cur_model in cls.models["data"]: if model == cur_model["model"]: return cur_model logger.warning("Wrong model: {}".format(model)) return defalut_responsepcl_pangu/pcl_pangu/onnx_inference/000077500000000000000000000000001443434453700200115ustar00rootroot00000000000000pcl_pangu/pcl_pangu/onnx_inference/__init__.py000066400000000000000000000000001443434453700221100ustar00rootroot00000000000000pcl_pangu/pcl_pangu/onnx_inference/conver_pt_onnx.py000066400000000000000000000073471443434453700234370ustar00rootroot00000000000000import numpy as np import random from .pangu.config import PanguConfig import os from pcl_pangu.context import check_context DEVICE = None def convert_2_onnx(input_model_path, model, past, DEVICE): import torch batch_size= 1 context_tokens = [1,2,3,4] past = torch.tensor(past) if DEVICE == 'cuda': dummy_input = {'input_ids': torch.tensor(context_tokens, device=DEVICE, dtype=torch.int).unsqueeze(0).repeat(batch_size, 1), 'past': past} else: dummy_input = (torch.tensor(context_tokens, device=DEVICE, dtype=torch.int).unsqueeze(0).repeat(batch_size, 1), None, None, None, past) # model_script = torch.jit.script(model) input_names = ["input_0","past"] output_names = ["output_0","output_1"] torch.onnx.export(model, dummy_input, input_model_path,input_names=input_names, opset_version=11, output_names=output_names, use_external_data_format=True, do_constant_folding=True, # will replace some of the ops that have all constant inputs, with pre-computed constant nodes. dynamic_axes={'input_0':[0,1],'past':[0,1,4],'output_0':[0,1,2]}) print('@@@@ onnx model saved to: ', input_model_path) def quantize_onnx(input_model_path: str, output_model_path: str): from onnxruntime.quantization import quantize_dynamic, QuantType quantized_model = quantize_dynamic( input_model_path, output_model_path, weight_type=QuantType.QUInt8, use_external_data_format=True, # extra_options={ # 'DisableShapeInference': True, # } ) print('@@@@ onnx quantized model saved to: ', output_model_path) def pt_2_onnx8(model_name, pt_path, model_config, onnx_ckpt_root_dir): backend_context = check_context() global DEVICE if backend_context == 'onnx-cpu': DEVICE = "cpu" elif backend_context == 'onnx-gpu': DEVICE = "cuda" print(f">>> using DEVICE: {DEVICE} >>>") import torch if DEVICE == 'cuda': from .pangu.model_onnx_gpu import Pangu else: from .pangu.model import Pangu from .pangu.utils import load_weight state_dict = torch.load(pt_path, map_location=DEVICE) print('@@@@ model_name: ', model_name) path_int8 = f'onnx_int8_{DEVICE}_{model_name}' path_int8 = os.path.join(onnx_ckpt_root_dir, path_int8) assert not os.path.exists(path_int8), f'\n@@@@ onnx_int8_model_path exists: {path_int8}' \ f'\n@@@@ The quantification process did not run, please remove it and run again!' os.system(f'rm -rf {path_int8}') os.system(f'mkdir -p {path_int8}') config = PanguConfig(model_config) model = Pangu(config) model = load_weight(model, state_dict) model.to(DEVICE) model.eval() layer_past_path = f'{model_name}_layer_past.npy' layer_past_path = os.path.join(path_int8, layer_past_path) input_ids = torch.tensor([13], device=DEVICE, dtype=torch.long).unsqueeze(0).repeat(1, 1) if DEVICE == 'cuda': logits, past = model({'input_ids': input_ids}) else: logits, past = model(input_ids, past=None, position_ids=None, lm_labels=None, token_type_ids=None) np.save(layer_past_path, past.detach().cpu().numpy()) path_onnx = f'onnx_{model_name}' path_onnx = os.path.join(path_int8, path_onnx) onnx_model_path = f'{path_onnx}/{model_name}.onnx' os.system(f'rm -rf {path_onnx}') os.system(f'mkdir -p {path_onnx}') convert_2_onnx(onnx_model_path, model, past, DEVICE) onnx_int8_model_path = f'{path_int8}/{model_name}_int8.onnx' quantize_onnx(onnx_model_path, onnx_int8_model_path) os.system(f'rm -rf {path_onnx}')pcl_pangu/pcl_pangu/onnx_inference/infer.py000066400000000000000000000067621443434453700215010ustar00rootroot00000000000000''' code by TaeHwan Jung(@graykode) Original Paper and repository here : https://github.com/openai/gpt-2 GPT2 Pytorch Model : https://github.com/huggingface/pytorch-pretrained-BERT ''' import numpy as np import os from .pangu.sample import generate import onnxruntime SESSION = None INT8_MODEL_PATH = None def create_model_for_provider(model_path: str, provider: str= 'CPUExecutionProvider',threads=2): from onnxruntime import GraphOptimizationLevel, InferenceSession, SessionOptions, get_all_providers assert provider in get_all_providers(), f"provider {provider} not found, {get_all_providers()}" # # Few properties that might have an impact on performances (provided by MS) options = SessionOptions() # options.intra_op_num_threads = int(os.environ.get('NUM_THREADS', 2)) # options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL # Load the model as a graph and prepare the CPU backend session = InferenceSession(model_path, options, providers=[provider]) # session = InferenceSession(model_path, providers=[provider]) session.disable_fallback() return session def onnx_infer_func(onnx_model_path: str,threads=2, backend='onnx-cpu'): input_names = ["input_0","past"] global SESSION if INT8_MODEL_PATH != onnx_model_path: if backend == 'onnx-cpu': SESSION = create_model_for_provider(onnx_model_path, 'CPUExecutionProvider', threads=threads) elif backend == 'onnx-gpu': SESSION = create_model_for_provider(onnx_model_path, 'CUDAExecutionProvider', threads=threads) def forward(input_ids, position_ids, token_type_ids, lm_labels, past): return SESSION.run(None, { input_names[0]: input_ids.astype(np.int32), input_names[1]: past, }) return forward def onnx_generate(input,onnx_int8_model_path,tokenizer,past_path, topk=1,top_p=0.9,threads=2,max_len=500, backend='onnx-cpu'): assert os.path.exists(onnx_int8_model_path), f"{onnx_int8_model_path} not found" assert onnx_int8_model_path.endswith('.onnx'), f"{onnx_int8_model_path} is not a onnx model" assert past_path.endswith('.npy'), f"{past_path} is not a npy file" past=np.load(past_path) infer_func = onnx_infer_func(onnx_int8_model_path,threads=threads, backend=backend) txt = generate(infer_func, input, tokenizer, past,max_len=max_len, top_p=top_p, top_k=topk,temperature=1.0,) return txt if __name__ == '__main__': from pcl_pangu.tokenizer.tokenization_jieba import get_tokenizer tokenizer = get_tokenizer() # generate_ = lambda text,max_len=100: generate(infer_func, text, tokenizer, past,max_len=max_len) # print(generate_('青椒肉丝的做法：\n')) # print(generate_('西红柿炒鸡蛋的做法：\n')) # print(generate_('上联：天地在我心中\n下联：')) # print(generate_('1+1=2;3+5=8;2+4=', max_len=2)) # print(generate_('默写古诗：\n白日依山尽，黄河入海流。\n床前明月光，', max_len=5)) # print(generate_('李大嘴：“各回各家，各找各妈！” \n佟掌柜：')) # print(generate_('中国的首都是北京\n日本的首都是东京\n美国的首都是')) # print(generate_('中国的四大发明有哪些？', 50)) # print(generate_('''乔布斯曾经说过：“''', 50)) # print(generate_('''老子曾经说过：“''', 50)) # print(generate_('''老子曾经说过：“''', 50)) pcl_pangu/pcl_pangu/onnx_inference/pangu/000077500000000000000000000000001443434453700211235ustar00rootroot00000000000000pcl_pangu/pcl_pangu/onnx_inference/pangu/config.py000066400000000000000000000040151443434453700227420ustar00rootroot00000000000000''' code by TaeHwan Jung(@graykode) Original Paper and repository here : https://github.com/openai/gpt-2 GPT2 Pytorch Model : https://github.com/huggingface/pytorch-pretrained-BERT ''' import pcl_pangu from pcl_pangu.model.alpha import MODEL_CONFIG class PanguConfig(object): def __init__( self, model_config, vocab_size_or_config_json_file=40_000, layer_norm_epsilon=1e-5, initializer_range=0.02, ): self.vocab_size = vocab_size_or_config_json_file self.layer_norm_epsilon = layer_norm_epsilon self.initializer_range = initializer_range config = model_config.model_config self.n_ctx = config['max_position_embeddings'] self.n_positions = config['seq_length'] self.n_embd = config['hidden_size'] self.n_layer = config['num_layers'] self.n_head = config['num_attention_heads'] # # class PanguConfig(object): # def __init__( # self, # vocab_size_or_config_json_file=40_000, # model_size='2B6', # layer_norm_epsilon=1e-5, # initializer_range=0.02, # ): # self.vocab_size = vocab_size_or_config_json_file # self.layer_norm_epsilon = layer_norm_epsilon # self.initializer_range = initializer_range # if model_size == '2B6': # self.model_2b6() # if model_size == '13B': # self.model_13b() # if model_size == '350M': # self.model_350m() # # def model_350m(self): # self.n_ctx = 1024 # self.n_positions = 1024 # self.n_embd = 2560 # self.n_layer = 24 # self.n_head = 16 # # def model_2b6(self): # self.n_ctx = 1024 # self.n_positions = 1024 # self.n_embd = 2560 # self.n_layer = 32 # self.n_head = 32 # # def model_13b(self): # self.n_ctx = 1024 # self.n_positions = 1024 # self.n_embd = 5120 # self.n_layer = 40 # self.n_head = 40pcl_pangu/pcl_pangu/onnx_inference/pangu/model.py000066400000000000000000000222211443434453700225740ustar00rootroot00000000000000''' code by TaeHwan Jung(@graykode) Original Paper and repository here : https://github.com/openai/gpt-2 GPT2 Pytorch Model : https://github.com/huggingface/pytorch-pretrained-BERT ''' import copy import torch import math import torch.nn as nn from torch.nn.parameter import Parameter from torch import functional as F def gelu(x): pi = torch.div(2,torch.FloatTensor([math.pi]).to(x.device)) return 0.5 * x * (1 + torch.tanh( torch.sqrt(pi) * (x + 0.044715 * torch.pow(x, 3)))) class LayerNorm(nn.Module): def __init__(self, hidden_size, eps=1e-12): """Construct a layernorm module in the TF style (epsilon inside the square root). """ super(LayerNorm, self).__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.bias = nn.Parameter(torch.zeros(hidden_size)) self.variance_epsilon = eps def forward(self, x): u = x.mean(-1, keepdim=True) s = (x - u).pow(2).mean(-1, keepdim=True) x = (x - u) / torch.sqrt(s + self.variance_epsilon) return self.weight * x + self.bias class Conv1D(nn.Module): def __init__(self, nf, nx, transpose=False): super(Conv1D, self).__init__() self.transpose = transpose self.nf = nf w = torch.empty(nx, nf) nn.init.normal_(w, std=0.02) self.weight = Parameter(w.t()) if transpose else Parameter(w) self.bias = Parameter(torch.zeros(nf)) def forward(self, x): size_out = x.size()[:-1] + (self.nf,) x = torch.addmm(self.bias, x.view(-1, x.size(-1)), self.weight.t() if self.transpose else self.weight) x = x.view(size_out) return x class Attention(nn.Module): def __init__(self, nx, n_ctx, config, scale=False): super(Attention, self).__init__() n_state = nx # in Attention: n_state=768 (nx=n_embd) # [switch nx => n_state from Block to Attention to keep identical to TF implem] assert n_state % config.n_head == 0 self.register_buffer("bias", torch.tril(torch.ones(n_ctx, n_ctx)).view(1, 1, n_ctx, n_ctx)) self.n_head = config.n_head self.split_size = n_state self.scale = scale self.query = Conv1D(nx, nx, transpose=True) self.key = Conv1D(nx, nx, transpose=True) self.value = Conv1D(nx, nx, transpose=True) self.dense = Conv1D(n_state, nx, transpose=True) self.softmax = nn.Softmax(dim=-1) def _attn(self, q, k, v): w = torch.matmul(q, k) if self.scale: w = torch.div(w,torch.sqrt( torch.FloatTensor([float(v.size(-1))]).to(v.device))) nd, ns = w.size(-2), w.size(-1) b = self.bias[:, :, ns-nd:ns, :ns] w = w * b - 1e10 * (1 - b) w = self.softmax(w) return torch.matmul(w, v) def merge_heads(self, x): x = x.permute(0, 2, 1, 3).contiguous() new_x_shape = x.size()[:-2] + (x.size(-2) * x.size(-1),) return x.view(new_x_shape) # in Tensorflow implem: fct merge_states def split_heads(self, x, k: bool=False): new_x_shape = x.size()[:-1] + (self.n_head, x.size(-1) // self.n_head) x = x.view(new_x_shape) # in Tensorflow implem: fct split_states if k: return x.permute(0, 2, 3, 1) # (batch, head, head_features, seq_length) else: return x.permute(0, 2, 1, 3) # (batch, head, seq_length, head_features) def forward(self, x, layer_past,query): if query is not None: query = self.query(query) else: query = self.query(x) key = self.key(x) value = self.value(x) query = self.split_heads(query) key = self.split_heads(key, k=True) value = self.split_heads(value) if layer_past is not None: past_key, past_value = layer_past[0].transpose(-2, -1), layer_past[1] # transpose back cf below key = torch.cat((past_key, key), dim=-1) value = torch.cat((past_value, value), dim=-2) present = torch.stack((key.transpose(-2, -1), value)) # transpose to have same shapes for stacking a = self._attn(query, key, value) a = self.merge_heads(a) a = self.dense(a) return a, present class MLP(nn.Module): def __init__(self, n_state, config): # in MLP: n_state=3072 (4 * n_embd) super(MLP, self).__init__() nx = config.n_embd self.dense_h_to_4h = Conv1D(n_state, nx, transpose=True) self.dense_4h_to_h = Conv1D(nx, n_state, transpose=True) self.act = gelu def forward(self, x): h = self.act(self.dense_h_to_4h(x)) h2 = self.dense_4h_to_h(h) return h2 class Att_block(nn.Module): def __init__(self, n_ctx, config, scale=False): super(Att_block, self).__init__() nx = config.n_embd self.input_layernorm = LayerNorm(nx, eps=config.layer_norm_epsilon) self.attention = Attention(nx, n_ctx, config, scale) self.post_attention_layernorm = LayerNorm(nx, eps=config.layer_norm_epsilon) self.mlp = MLP(4 * nx, config) def forward(self, x, layer_past, query): x_ln = self.input_layernorm(x) a, present = self.attention(x_ln, layer_past=layer_past, query=query) x = x + a m = self.mlp(self.post_attention_layernorm(x)) x = x + m return x, present class Embendding(nn.Module): def __init__(self, config): super(Embendding, self).__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.n_embd) self.position_embeddings = nn.Embedding(config.n_positions, config.n_embd) self.top_query_embeddings = nn.Embedding(config.n_positions, config.n_embd) def forward(self, input_ids, position_ids): inputs_embeds = self.word_embeddings(input_ids) position_embeds = self.position_embeddings(position_ids) top_query_embeddins = self.top_query_embeddings(position_ids) return inputs_embeds, position_embeds, top_query_embeddins class Transformer(nn.Module): def __init__(self, config): super(Transformer, self).__init__() self.n_layer = config.n_layer self.n_embd = config.n_embd self.n_vocab = config.vocab_size block = Att_block(config.n_ctx, config, scale=True) self.layers = nn.ModuleList([copy.deepcopy(block) for _ in range(config.n_layer-1)]) self.topQueryLayer = Att_block(config.n_ctx, config, scale=True) self.final_layernorm = LayerNorm(config.n_embd, eps=config.layer_norm_epsilon) def set_embeddings_weights(self, model_embeddings_weights): embed_shape = model_embeddings_weights.shape self.decoder = nn.Linear(embed_shape[1], embed_shape[0], bias=False) self.decoder.weight = model_embeddings_weights # Tied weights def forward(self, input_ids, position_ids, token_type_ids, past, embendding): if past is None: past_length = 0 past = [None] * self.n_layer else: past_length = past[0][0].size(-2) if position_ids is None: position_ids = torch.arange(past_length, input_ids.size(-1) + past_length, dtype=torch.long, device=input_ids.device) position_ids = position_ids.unsqueeze(0).expand_as(input_ids) input_shape = input_ids.size() input_ids = input_ids.view(-1, input_ids.size(-1)) position_ids = position_ids.view(-1, position_ids.size(-1)) inputs_embeds, position_embeds, top_query_embeddins = embendding(input_ids, position_ids) if token_type_ids is not None: token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) token_type_embeds = self.word_embenddings(token_type_ids) else: token_type_embeds = 0 hidden_states = inputs_embeds + position_embeds + token_type_embeds presents = [] for i,block in enumerate(self.layers): layer_past = past[i] hidden_states, present = block(hidden_states, layer_past, query=None) presents.append(present) hidden_states = self.final_layernorm(hidden_states) layer_past = past[i+1] hidden_states, present = self.topQueryLayer(hidden_states, layer_past, query=top_query_embeddins) presents.append(present) output_shape = input_shape + (hidden_states.size(-1),) return hidden_states.view(output_shape), torch.stack(presents) class Pangu(nn.Module): def __init__(self, config): super(Pangu, self).__init__() self.embedding = Embendding(config) self.transformer = Transformer(config) self.loss_fct = nn.CrossEntropyLoss(ignore_index=-1) def forward(self, input_ids, position_ids, token_type_ids, lm_labels, past): hidden_states, presents = self.transformer(input_ids, position_ids, token_type_ids, past,self.embedding) lm_logits = torch.matmul(hidden_states, self.embedding.word_embeddings.weight.t()) if lm_labels is not None: loss = self.loss_fct(lm_logits.view(-1, lm_logits.size(-1)), lm_labels.view(-1)) return loss return lm_logits, presentspcl_pangu/pcl_pangu/onnx_inference/pangu/model_onnx_gpu.py000066400000000000000000000213201443434453700245100ustar00rootroot00000000000000''' code by TaeHwan Jung(@graykode) Original Paper and repository here : https://github.com/openai/gpt-2 GPT2 Pytorch Model : https://github.com/huggingface/pytorch-pretrained-BERT ''' import copy import torch import math import torch.nn as nn from torch.nn.parameter import Parameter from torch import functional as F def gelu(x): pi = torch.div(2,torch.FloatTensor([math.pi]).to(x.device)) return 0.5 * x * (1 + torch.tanh( torch.sqrt(pi) * (x + 0.044715 * torch.pow(x, 3)))) class LayerNorm(nn.Module): def __init__(self, hidden_size, eps=1e-12): """Construct a layernorm module in the TF style (epsilon inside the square root). """ super(LayerNorm, self).__init__() self.weight = nn.Parameter(torch.ones(hidden_size)) self.bias = nn.Parameter(torch.zeros(hidden_size)) self.variance_epsilon = eps def forward(self, x): u = x.mean(-1, keepdim=True) s = (x - u).pow(2).mean(-1, keepdim=True) x = (x - u) / torch.sqrt(s + self.variance_epsilon) return self.weight * x + self.bias class Conv1D(nn.Module): def __init__(self, nf, nx, transpose=False): super(Conv1D, self).__init__() self.transpose = transpose self.nf = nf w = torch.empty(nx, nf) nn.init.normal_(w, std=0.02) self.weight = Parameter(w.t()) if transpose else Parameter(w) self.bias = Parameter(torch.zeros(nf)) def forward(self, x): size_out = x.size()[:-1] + (self.nf,) x = torch.addmm(self.bias, x.view(-1, x.size(-1)), self.weight.t() if self.transpose else self.weight) x = x.view(size_out) return x class Attention(nn.Module): def __init__(self, nx, n_ctx, config, scale=False): super(Attention, self).__init__() n_state = nx # in Attention: n_state=768 (nx=n_embd) # [switch nx => n_state from Block to Attention to keep identical to TF implem] assert n_state % config.n_head == 0 self.register_buffer("bias", torch.tril(torch.ones(n_ctx, n_ctx)).view(1, 1, n_ctx, n_ctx)) self.n_head = config.n_head self.split_size = n_state self.scale = scale self.query = Conv1D(nx, nx, transpose=True) self.key = Conv1D(nx, nx, transpose=True) self.value = Conv1D(nx, nx, transpose=True) self.dense = Conv1D(n_state, nx, transpose=True) self.softmax = nn.Softmax(dim=-1) def _attn(self, q, k, v): w = torch.matmul(q, k) if self.scale: w = torch.div(w,torch.sqrt( torch.FloatTensor([float(v.size(-1))]).to(v.device))) nd, ns = w.size(-2), w.size(-1) b = self.bias[:, :, ns-nd:ns, :ns] w = w * b - 1e10 * (1 - b) w = self.softmax(w) return torch.matmul(w, v) def merge_heads(self, x): x = x.permute(0, 2, 1, 3).contiguous() new_x_shape = x.size()[:-2] + (x.size(-2) * x.size(-1),) return x.view(new_x_shape) # in Tensorflow implem: fct merge_states def split_heads(self, x, k: bool=False): new_x_shape = x.size()[:-1] + (self.n_head, x.size(-1) // self.n_head) x = x.view(new_x_shape) # in Tensorflow implem: fct split_states if k: return x.permute(0, 2, 3, 1) # (batch, head, head_features, seq_length) else: return x.permute(0, 2, 1, 3) # (batch, head, seq_length, head_features) def forward(self, x, layer_past,query): if query is not None: query = self.query(query) else: query = self.query(x) key = self.key(x) value = self.value(x) query = self.split_heads(query) key = self.split_heads(key, k=True) value = self.split_heads(value) if layer_past is not None: past_key, past_value = layer_past[0].transpose(-2, -1), layer_past[1] # transpose back cf below key = torch.cat((past_key, key), dim=-1) value = torch.cat((past_value, value), dim=-2) present = torch.stack((key.transpose(-2, -1), value)) # transpose to have same shapes for stacking a = self._attn(query, key, value) a = self.merge_heads(a) a = self.dense(a) return a, present class MLP(nn.Module): def __init__(self, n_state, config): # in MLP: n_state=3072 (4 * n_embd) super(MLP, self).__init__() nx = config.n_embd self.dense_h_to_4h = Conv1D(n_state, nx, transpose=True) self.dense_4h_to_h = Conv1D(nx, n_state, transpose=True) self.act = gelu def forward(self, x): h = self.act(self.dense_h_to_4h(x)) h2 = self.dense_4h_to_h(h) return h2 class Att_block(nn.Module): def __init__(self, n_ctx, config, scale=False): super(Att_block, self).__init__() nx = config.n_embd self.input_layernorm = LayerNorm(nx, eps=config.layer_norm_epsilon) self.attention = Attention(nx, n_ctx, config, scale) self.post_attention_layernorm = LayerNorm(nx, eps=config.layer_norm_epsilon) self.mlp = MLP(4 * nx, config) def forward(self, x, layer_past, query): x_ln = self.input_layernorm(x) a, present = self.attention(x_ln, layer_past=layer_past, query=query) x = x + a m = self.mlp(self.post_attention_layernorm(x)) x = x + m return x, present class Embendding(nn.Module): def __init__(self, config): super(Embendding, self).__init__() self.word_embeddings = nn.Embedding(config.vocab_size, config.n_embd) self.position_embeddings = nn.Embedding(config.n_positions, config.n_embd) self.top_query_embeddings = nn.Embedding(config.n_positions, config.n_embd) def forward(self, input_ids, position_ids): input_ids = input_ids.long() inputs_embeds = self.word_embeddings(input_ids) position_embeds = self.position_embeddings(position_ids) top_query_embeddins = self.top_query_embeddings(position_ids) return inputs_embeds, position_embeds, top_query_embeddins class Transformer(nn.Module): def __init__(self, config): super(Transformer, self).__init__() self.n_layer = config.n_layer self.n_embd = config.n_embd self.n_vocab = config.vocab_size block = Att_block(config.n_ctx, config, scale=True) self.layers = nn.ModuleList([copy.deepcopy(block) for _ in range(config.n_layer-1)]) self.topQueryLayer = Att_block(config.n_ctx, config, scale=True) self.final_layernorm = LayerNorm(config.n_embd, eps=config.layer_norm_epsilon) def set_embeddings_weights(self, model_embeddings_weights): embed_shape = model_embeddings_weights.shape self.decoder = nn.Linear(embed_shape[1], embed_shape[0], bias=False) self.decoder.weight = model_embeddings_weights # Tied weights def forward(self, input_ids, past, embendding): if past is None: past_length = 0 past = [None] * self.n_layer else: past_length = past[0][0].size(-2) position_ids = torch.arange(past_length, input_ids.size(-1) + past_length, dtype=torch.long, device=input_ids.device) position_ids = position_ids.unsqueeze(0).expand_as(input_ids) input_shape = input_ids.size() input_ids = input_ids.view(-1, input_ids.size(-1)) position_ids = position_ids.view(-1, position_ids.size(-1)) inputs_embeds, position_embeds, top_query_embeddins = embendding(input_ids, position_ids) token_type_embeds = 0 hidden_states = inputs_embeds + position_embeds + token_type_embeds presents = [] for i,block in enumerate(self.layers): layer_past = past[i] hidden_states, present = block(hidden_states, layer_past, query=None) presents.append(present) hidden_states = self.final_layernorm(hidden_states) layer_past = past[i+1] hidden_states, present = self.topQueryLayer(hidden_states, layer_past, query=top_query_embeddins) presents.append(present) output_shape = input_shape + (hidden_states.size(-1),) return hidden_states.view(output_shape), torch.stack(presents) class Pangu(nn.Module): def __init__(self, config): super(Pangu, self).__init__() self.embedding = Embendding(config) self.transformer = Transformer(config) self.loss_fct = nn.CrossEntropyLoss(ignore_index=-1) def forward(self, input_dict): input_ids = input_dict['input_ids'] past = input_dict.get('past') hidden_states, presents = self.transformer(input_ids, past, self.embedding) lm_logits = torch.matmul(hidden_states, self.embedding.word_embeddings.weight.t()) return lm_logits, presentspcl_pangu/pcl_pangu/onnx_inference/pangu/sample.py000066400000000000000000000114561443434453700227650ustar00rootroot00000000000000''' code by TaeHwan Jung(@graykode) Original Paper and repository here : https://github.com/openai/gpt-2 GPT2 Pytorch Model : https://github.com/huggingface/pytorch-pretrained-BERT ''' import torch import torch.nn.functional as F from tqdm import trange import numpy as np from scipy.special import softmax import time def partition_arg_topK(matrix, K, axis=0): """ perform topK based on np.argpartition :param matrix: to be sorted :param K: select and sort the top K items :param axis: 0 or 1. dimension to be sorted. :return: """ a_part = np.argpartition(matrix, K, axis=axis) if axis == 0: row_index = np.arange(matrix.shape[1 - axis]) a_sec_argsort_K = np.argsort(matrix[a_part[0:K, :], row_index], axis=axis) return a_part[0:K, :][a_sec_argsort_K, row_index] else: column_index = np.arange(matrix.shape[1 - axis])[:, None] a_sec_argsort_K = np.argsort(matrix[column_index, a_part[:, 0:K]], axis=axis) return a_part[:, 0:K][column_index, a_sec_argsort_K] def top_k_logits(logits, k): if k == 0: return logits values, _ = torch.topk(logits, k) min_values = values[:, -1] return torch.where(logits < min_values, torch.ones_like(logits, dtype=logits.dtype) * -1e10, logits) def sample_sequence(model, length, start_token=None, batch_size=None, context=None, temperature=1, top_k=0, device='cuda', sample=True, past = None): if start_token is None: assert context is not None, 'Specify exactly one of start_token and context!' context = torch.tensor(context, device=device, dtype=torch.long).unsqueeze(0).repeat(batch_size, 1) else: assert context is None, 'Specify exactly one of start_token and context!' context = torch.full((batch_size, 1), start_token, device=device, dtype=torch.long) prev = context output = context with torch.no_grad(): for i in trange(length): logits, past = model(prev, past=past, position_ids=None, lm_labels=None, token_type_ids=None) logits = torch.tensor(logits).to(device) logits = logits[:, -1, :] / temperature logits = top_k_logits(logits, k=top_k) log_probs = F.softmax(logits, dim=-1) if sample: prev = torch.multinomial(log_probs, num_samples=1) else: _, prev = torch.topk(log_probs, k=1, dim=-1) output = torch.cat((output, prev), dim=1) return output def timer(func): def wrapper(*args, **kwargs): import time start = time.time() res, num = func(*args, **kwargs) num += 1e-10 end = time.time() print(f'Generating {int(num)} tokens costs {end - start}s') print(f'Per token costs {(end - start)/num}s') return res return wrapper def generate( model, text, tokenizer, past, max_len = 100, temperature = 1.0, top_p = 0.95, top_k = 50, eod=None, additional_eod=[], ban = [] ): if eod is None: eod = [tokenizer.eod_id, tokenizer.eot_id] ids = tokenizer.encode(text) len_input = len(ids) predict_ids = [] kv_cache = None next_token = np.array([ids], dtype=np.int32) start_time = time.time() for i in range(max_len): logits, past = model(next_token, past=past, position_ids=None, lm_labels=None, token_type_ids=None) for x in ban: logits[:, -1, x] = -9999 logits = logits / temperature scores = softmax(logits[:, -1, :]) next_probs = np.sort(scores)[:, ::-1] if top_p > 0.0 and top_p < 1.0: next_probs = next_probs[:, :int(next_probs.shape[1] * (1 - top_p))] if top_k > 0 and top_k < next_probs.shape[1]: next_probs = next_probs[:, :top_k] next_probs_1 = next_probs / next_probs.sum(axis=1).reshape((-1, 1)) next_tokens = np.argsort(scores)[:, ::-1] if top_p > 0.0 and top_p < 1.0: next_tokens = next_tokens[:, :int(next_tokens.shape[1] * (1 - top_p))] if top_k > 0 and top_k < next_tokens.shape[1]: next_tokens = next_tokens[:, :top_k] next_token = np.random.choice(next_tokens[0], p=next_probs_1[0]) if eod is not None and next_token in eod: break if next_token in additional_eod or tokenizer.decode([int(next_token)]) in additional_eod: break predict_ids.append(next_token) next_token = np.array([[next_token]], dtype=np.int32) end_time = time.time() inference_tokens_number = len(predict_ids) print(">>> inference speed: {:.4f}ms/token >>>\n".format(1000.0 * (end_time - start_time)/float(inference_tokens_number))) return text + tokenizer.decode([int(x) for x in predict_ids]) pcl_pangu/pcl_pangu/onnx_inference/pangu/utils.py000066400000000000000000000025721443434453700226430ustar00rootroot00000000000000''' code by TaeHwan Jung(@graykode) Original Paper and repository here : https://github.com/openai/gpt-2 GPT2 Pytorch Model : https://github.com/huggingface/pytorch-pretrained-BERT ''' import logging import torch logger = logging.getLogger(__name__) def load_weight(model, state_dict): state_dict_new = {} def map_keys(state_dict, prefix): for key in state_dict.keys(): if isinstance(state_dict[key], dict): if key == 'task_embedding': map_keys(state_dict[key], 'embedding' if prefix == '' else prefix + '.' + 'embedding') elif key == 'model' or key == 'language_model': map_keys(state_dict[key], prefix) else: map_keys(state_dict[key], key if prefix == '' else prefix + '.' + key) else: state_dict_new[key if prefix == '' else prefix + '.' + key] = state_dict[key] map_keys(state_dict, prefix='') missing_keys = [] unexpected_keys = [] error_msgs = [] local_metadata = {} prefix = '' vocab_size = model.state_dict()['embedding.word_embeddings.weight'].shape[0] state_dict_new['embedding.word_embeddings.weight'] = state_dict_new['embedding.word_embeddings.weight'][:vocab_size] model.load_state_dict(state_dict_new,strict=False) state_dict_model = model.state_dict() return modelpcl_pangu/pcl_pangu/onnx_inference/test.py000066400000000000000000000170021443434453700213420ustar00rootroot00000000000000''' code by TaeHwan Jung(@graykode) Original Paper and repository here : https://github.com/openai/gpt-2 GPT2 Pytorch Model : https://github.com/huggingface/pytorch-pretrained-BERT ''' import os import sys import torch import random import argparse import numpy as np from pangu.model import (Pangu) from pangu.utils import load_weight from pangu.config import PanguConfig from pangu.sample import sample_sequence, generate import onnxruntime as ort from onnxruntime.quantization import quantize_dynamic, QuantType from onnxruntime import GraphOptimizationLevel, InferenceSession, SessionOptions, get_all_providers from tokenization_jieba import JIEBATokenizer tokenizer = JIEBATokenizer( 'bpe_4w_pcl/vocab.vocab', 'bpe_4w_pcl/vocab.model') def create_model_for_provider(model_path: str, provider: str= 'CPUExecutionProvider') -> InferenceSession: assert provider in get_all_providers(), f"provider {provider} not found, {get_all_providers()}" # Few properties that might have an impact on performances (provided by MS) options = SessionOptions() options.intra_op_num_threads = int(os.environ.get('NUM_THREADS', 2)) print(f"@@@@ Using {options.intra_op_num_threads} threads for inference") options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL # Load the model as a graph and prepare the CPU backend session = InferenceSession(model_path, options, providers=[provider]) session.disable_fallback() return session def convert_2_onnx(input_model_path, model, past): batch_size= 1 context_tokens = [1,2,3,4] past = torch.tensor(past) dummy_input = (torch.tensor(context_tokens, device="cpu", dtype=torch.int).unsqueeze(0).repeat(batch_size, 1), None,None,None,past) # model_script = torch.jit.script(model) input_names = ["input_0","past"] output_names = ["output_0","output_1"] torch.onnx.export(model, dummy_input, input_model_path,input_names=input_names, opset_version=11, output_names=output_names, use_external_data_format=True, dynamic_axes={'input_0':[0,1],'past':[0,1,4],'output_0':[0,1,2]}) print('@@@@ onnx model saved to: ', input_model_path) def quantize_onnx(input_model_path: str, output_model_path: str): quantized_model = quantize_dynamic( input_model_path, output_model_path, weight_type=QuantType.QUInt8, use_external_data_format=True, extra_options={ 'DisableShapeInference': True, } ) print('@@@@ onnx quantized model saved to: ', output_model_path) def onnx_infer_func(onnx_model_path: str): input_names = ["input_0","past"] session = create_model_for_provider(onnx_model_path, 'CPUExecutionProvider') def forward(input_ids, position_ids, token_type_ids, lm_labels, past): return session.run(None, { input_names[0]: input_ids.astype(np.int32), input_names[1]: past, }) return forward def torch_infer_func(model): def forward(input_ids, position_ids, token_type_ids, lm_labels, past): input_ids = torch.tensor(input_ids) past = torch.tensor(past) logits, past = model(input_ids, past=past, position_ids=None, lm_labels=None, token_type_ids=None) logits = logits.detach().cpu().numpy() return logits, past return forward def text_generator(model_name, model_size, onnx_ckpt_root_dir='', infer_backend='onnx',state_dict=None): seed = random.randint(0, 2147483647) np.random.seed(seed) torch.random.manual_seed(seed) torch.cuda.manual_seed(seed) # device = torch.device("cuda" if torch.cuda.is_available() else "cpu") device = "cpu" print('@@@@ model_name: ', model_name) if state_dict is not None: # Load Model config = PanguConfig(model_size=model_size) model = Pangu(config) model = load_weight(model, state_dict) model.to(device) model.eval() layer_past_path = f'{model_name}_layer_past.npy' if not os.path.exists(layer_past_path): input_ids = torch.tensor([13], device=device, dtype=torch.long).unsqueeze(0).repeat(1, 1) logits, past = model(input_ids, past=None, position_ids=None, lm_labels=None, token_type_ids=None) np.save(layer_past_path, past.detach().cpu().numpy()) past = np.load(layer_past_path) path = f'onnx_{model_name}' path = os.path.join(onnx_ckpt_root_dir, path) onnx_model_path = f'{path}/{model_name}.onnx' if not os.path.exists(onnx_model_path): os.system(f'rm -rf {path}') os.system(f'mkdir -p {path}') convert_2_onnx(onnx_model_path, model, past) path = f'onnx_int8_{model_name}' path = os.path.join(onnx_ckpt_root_dir, path) onnx_int8_model_path = f'{path}/{model_name}_int8.onnx' if not os.path.exists(onnx_int8_model_path): os.system(f'rm -rf {path}') os.system(f'mkdir -p {path}') quantize_onnx(onnx_model_path, onnx_int8_model_path) if infer_backend == 'onnx': print('@@@@ onnx running on device: ', ort.get_device()) print('@@@@ onnx inference') infer_func = onnx_infer_func(onnx_int8_model_path) elif infer_backend == 'torch': print('@@@@ torch inference') infer_func = torch_infer_func(model) generate_ = lambda text,max_len=100: generate(infer_func, text, tokenizer, past,max_len=max_len) print(generate_('青椒肉丝的做法：\n')) print(generate_('西红柿炒鸡蛋的做法：\n')) print(generate_('上联：天地在我心中\n下联：')) print(generate_('1+1=2;3+5=8;2+4=', max_len=2)) print(generate_('默写古诗：\n白日依山尽，黄河入海流。\n床前明月光，', max_len=5)) print(generate_('李大嘴：“各回各家，各找各妈！” \n佟掌柜：')) print(generate_('中国的首都是北京\n日本的首都是东京\n美国的首都是')) print(generate_('中国的四大发明有哪些？', 50)) print(generate_('''乔布斯曾经说过：“''', 50)) print(generate_('''老子曾经说过：“''', 50)) print(generate_('''老子曾经说过：“''', 50)) if __name__ == '__main__': # model_name = 'pangu_evolution' # model_size = '2b6' # text_generator(model_name, model_size) # # model_path = '/home/yands/tmp/models/pangu-alpha-evolution_2.6b_fp16/iter_0055000/mp_rank_00/model_optim_rng.pt' # # assert os.path.exists(model_path) # # state_dict = torch.load(model_path, map_location='cpu') # # text_generator(model_name, model_size, infer_backend='torch',state_dict=state_dict) model_name = 'pangu_alpha_2b6' model_size = '2b6' text_generator(model_name, model_size) # model_path = '/raid0/yands/tmp/models/panguAlpha_2.6b_NumpyCkpt/merged/iter_0076000/mp_rank_00/model_optim_rng.pt' # assert os.path.exists(model_path) # state_dict = torch.load(model_path, map_location='cpu') # text_generator(model_name, model_size, infer_backend='torch',state_dict=state_dict) # model_name = 'pangu_alpha_13b' # model_size = '13b' # # text_generator(model_name, model_size) # model_path = '/raid0/yands/tmp/models/panguAlpha_13b_fp16_NumpyCkpt/merged/iter_0076000/mp_rank_00/model_optim_rng.pt' # assert os.path.exists(model_path) # state_dict = torch.load(model_path, map_location='cpu') # text_generator(model_name, model_size, infer_backend='torch',state_dict=state_dict) pcl_pangu/pcl_pangu/tokenizer/000077500000000000000000000000001443434453700170235ustar00rootroot00000000000000pcl_pangu/pcl_pangu/tokenizer/__init__.py000066400000000000000000000003551443434453700211370ustar00rootroot00000000000000import os current_dir = os.path.dirname(os.path.abspath(__file__)) vocab_4w = os.path.join(current_dir, "bpe_4w_pcl/vocab") vocab_13w = os.path.join(current_dir, "spm_13w/spm.128k.model.1") __all__ = [ "vocab_4w", "vocab_13w" ]pcl_pangu/pcl_pangu/tokenizer/__pycache__/000077500000000000000000000000001443434453700212335ustar00rootroot00000000000000pcl_pangu/pcl_pangu/tokenizer/__pycache__/__init__.cpython-38.pyc000066400000000000000000000006061443434453700254240ustar00rootroot00000000000000U b@sDddlZejejeZejedZejedZddgZ dS)Nzbpe_4w_pcl/vocabzspm_13w/spm.128k.model.1vocab_4w vocab_13w) ospathdirnameabspath__file__ current_dirjoinrr__all__r r MD:\并行平台\codes\AISyn_develop\pcl_pangu\pcl_pangu\tokenizer\__init__.pys pcl_pangu/pcl_pangu/tokenizer/bpe_4w_pcl/000077500000000000000000000000001443434453700210415ustar00rootroot00000000000000pcl_pangu/pcl_pangu/tokenizer/bpe_4w_pcl/vocab.model000066400000000000000000032661211443434453700231700ustar00rootroot00000000000000     ▃ ▂           ▁,0 ▁的H0S ▁。Om ▁~ ▁、X ▁了 ▁是YR ▁:c ▁在oj ▁”q ▁“t ▁我|y ▁和o ▁有l ▁也I ▁不´ ▁他5 ▁你I ▁?; ▁! ▁就k& ▁) ▁(= ▁都] ▁.[ ▁-c ▁人O ▁这] ▁中*o ▁上 ▁为 ▁对/W ▁她A ▁等 ▁... ▁与:D  ▁一个3 ▁说 ▁;3 ▁年] ▁要b ▁3X ▁到̸ ▁着h ▁将G ▁一 ▁大  ▁我们G ▁会)!  ▁自己t ▁而 ▁那3 ▁月c ▁或  ▁可以 ▁被i^  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z>6Ԟʓ|Mʾ.ty/PG8-ei8!Y,\<!{V[xwg{~| z"ŕ+ݣs2krhnJ֋lte)1T^ {ECWE3)oADR_[u9T XpOF]÷\ny gGh<|eYI.skr,4^"}ib:>QB(ܣŃLW z#s-V\a vcJ 9]2b D*Ǹ.sJ3=9)Ag?>t!B :!BA'BB!tB!P ! :!Q, tB>BA'a02I(B <`:a^~% %w\jbXmBJjPBkB!$\p BB!N! :!B(BB!tB!P !BA'B(BB!N! :!BA'BB!tB!P ! :!B(BB!N!P !BA'BB!tB!N! :!B(BB! 0r^IENDB`pcl_pangu/examples/000077500000000000000000000000001443434453700146575ustar00rootroot00000000000000pcl_pangu/examples/__init__.py000066400000000000000000000000001443434453700167560ustar00rootroot00000000000000pcl_pangu/onnx_output_gpu_int8.py000066400000000000000000000007371443434453700176410ustar00rootroot00000000000000from pcl_pangu.context import set_context from pcl_pangu.model import alpha, evolution, mPangu from pcl_pangu.model_converter import pt_2_onnx8 if __name__ == '__main__': set_context('onnx-gpu') config = alpha.model_config_onnx(model='2B6') pt_2_onnx8(model_name='alpha-2b6', pt_path='/userhome/Pangu-alpha_2.6B_mgt/iter_0001000/mp_rank_00/model_optim_rng.pt', model_config=config, onnx_ckpt_root_dir='/userhome/yizx_v100_pangu2b6') pcl_pangu/pcl_pangu/000077500000000000000000000000001443434453700150115ustar00rootroot00000000000000pcl_pangu/pcl_pangu/__init__.py000066400000000000000000000003051443434453700171200ustar00rootroot00000000000000from . import context, dataset, model, online, model_converter, onnx_inference __all__ = [ "context", "dataset", "model", "model_converter", "online", "onnx_inference", ] pcl_pangu/pcl_pangu/__pycache__/000077500000000000000000000000001443434453700172215ustar00rootroot00000000000000pcl_pangu/pcl_pangu/__pycache__/__init__.cpython-38.pyc000066400000000000000000000004151443434453700234100ustar00rootroot00000000000000U bh@s"ddlmZmZmZdddgZdS))contextdatasetmodelrrrN)rrr__all__rrCD:\并行平台\codes\AISyn_develop\pcl_pangu\pcl_pangu\__init__.pyspcl_pangu/pcl_pangu/context/000077500000000000000000000000001443434453700164755ustar00rootroot00000000000000pcl_pangu/pcl_pangu/context/__init__.py000066400000000000000000000003711443434453700206070ustar00rootroot00000000000000from .context import ( set_context, get_context, _confirm_id_token, _CONTEXT ) from .check_context import check_context __all__ = [ "set_context", "get_context", "_confirm_id_token", "_CONTEXT", "check_context" ]pcl_pangu/pcl_pangu/context/__pycache__/000077500000000000000000000000001443434453700207055ustar00rootroot00000000000000pcl_pangu/pcl_pangu/context/__pycache__/__init__.cpython-38.pyc000066400000000000000000000005711443434453700250770ustar00rootroot00000000000000U b@s6ddlmZmZmZmZddlmZdddddgZdS) ) set_context get_context_confirm_id_token_CONTEXT) check_contextrrrrrN)contextrrrrr__all__r r KD:\并行平台\codes\AISyn_develop\pcl_pangu\pcl_pangu\context\__init__.pys pcl_pangu/pcl_pangu/context/__pycache__/check_context.cpython-38.pyc000066400000000000000000000007441443434453700261630ustar00rootroot00000000000000U b:@sddlmZddZdS)) get_contextcCs$t}t|jtstd|j}|S)Nz3You need to use easypangu.context.set_context first)r isinstancebackendstr ImportError)contextZBACKENDrPD:\并行平台\codes\AISyn_develop\pcl_pangu\pcl_pangu\context\check_context.py check_contexts  r N)Zpcl_pangu.contextrr rrrr s pcl_pangu/pcl_pangu/context/__pycache__/context.cpython-38.pyc000066400000000000000000000013431443434453700250220ustar00rootroot00000000000000U b@s:ddlmZeddddgaddZdd d Zd d Zd S)) namedtupleCONTEXTbackendidtokencCsdSNrrrJD:\并行平台\codes\AISyn_develop\pcl_pangu\pcl_pangu\context\context.py_confirm_id_tokensr pytorchcCs(|dks t|t_|t_|t_tdS)N)r Z mindspore)AssertionError_CONTEXTrrrr )rrrrrr set_context s  rcCstSr)r rrrr get_contextsrN)r rr) collectionsrr r rrrrrr s  pcl_pangu/pcl_pangu/context/check_context.py000066400000000000000000000004571443434453700216760ustar00rootroot00000000000000from pcl_pangu.context import get_context def check_context(): ### # user identify ### context = get_context() if not isinstance(context.backend, str): raise ImportError("You need to use easypangu.context.set_context first") BACKEND = context.backend return BACKENDpcl_pangu/pcl_pangu/context/context.py000066400000000000000000000006621443434453700205370ustar00rootroot00000000000000from collections import namedtuple _CONTEXT = namedtuple('CONTEXT',['backend','id','token']) def _confirm_id_token(): pass def set_context(backend='pytorch',id=0,token=0): assert backend in ['pytorch', 'mindspore', 'onnx-cpu', 'onnx-gpu'] global _CONTEXT _CONTEXT.backend = backend _CONTEXT.id = id _CONTEXT.token = token #id token 验证 _confirm_id_token() def get_context(): return _CONTEXTpcl_pangu/pcl_pangu/dataset/000077500000000000000000000000001443434453700164365ustar00rootroot00000000000000pcl_pangu/pcl_pangu/dataset/__init__.py000066400000000000000000000002141443434453700205440ustar00rootroot00000000000000from .txt2bin import ( txt2bin ) from .txt2mindrecord import ( txt2mindrecord ) __all__ = [ "txt2bin", "txt2mindrecord", ] pcl_pangu/pcl_pangu/dataset/__pycache__/000077500000000000000000000000001443434453700206465ustar00rootroot00000000000000pcl_pangu/pcl_pangu/dataset/__pycache__/__init__.cpython-38.pyc000066400000000000000000000004151443434453700250350ustar00rootroot00000000000000U b@s$ddlmZddlmZddgZdS))txt2bin)txt2mindrecordrrN)rr__all__rrKD:\并行平台\codes\AISyn_develop\pcl_pangu\pcl_pangu\dataset\__init__.pys  pcl_pangu/pcl_pangu/dataset/__pycache__/txt2bin.cpython-38.pyc000066400000000000000000000052441443434453700246750ustar00rootroot00000000000000U H(b@s@ddlZddlZddlmZd ddZedkr txt2bin only support 'pytorch' backend, U need to set pcl_pangu.context.set_context backend correctly)get_argsnltk_availableEncoder package_fileindexed_datasetzL> You need to set input_glob[str] correctly! absolute path is recommended...zO> You need to set output_prefix[str] correctly! absolute path is recommended...zL> You need to set vocab_file[str] correctly! absolute path is recommended...rZOpeningZpunktT)quiet encoded_docsZdocumentsentencezOutput prefix: z {}{}_{}.binz {}{}_{}.idx)impl vocab_sizezTime to startup:)startiz Processed z documents(z docs/s, z MB/s).)filezPreprocess data using {}s)+Zpcl_pangu.contextr ImportErrortorchZpreprocess_data_pangurrrr r globnltkmultiprocessingtime isinstancestrr vocab_fileinput output_prefixprintiglobZsplit_sentencesZdownloadZPoolZworkersimapencodeZ json_keysformatZ make_builderZ dataset_implZ tokenizerr enumerateitemsZadd_itemZ IntTensorZ end_document log_intervalsysstderrfinalize)% input_globrrrBACKENDrrrrr r rrrargsZ startup_startZ file_iterencoderpoolrlevelZoutput_bin_filesZoutput_idx_filesZbuilderskeyZ startup_endZ proc_startZtotal_bytes_processedidocZbytes_processedZ sentencesrcurrentelapsedZmbsend_timer7JD:\并行平台\codes\AISyn_develop\pcl_pangu\pcl_pangu\dataset\txt2bin.pytxt2bins          r9__main__r )r+rr)r)rr(pcl_pangu.tokenizerrr9__name__r7r7r7r8s   Xpcl_pangu/pcl_pangu/dataset/__pycache__/txt2mindrecord.cpython-38.pyc000066400000000000000000000112631443434453700262510ustar00rootroot00000000000000U ˧bW@s>ddlZejejeZdddZedkr:eddddS) Nvocab_4wc) Csddlm}|}td||dks0tdddlmam}m}m }m }m } m } m } m} m} m}m}m}m}ddl}ddl}ddlm}t|tr|d krtd t|tr|d krtd t|tr|d krtd d |ksd|kr~|j|}t|dkrtdtd|rtd}tj|s8t ||D]&}|!dd}|j"|||qtd$|!n|j+d&krvt-|.|j#}#|#/|0d'|1|#t2|j3!d(d}$t2|j3!d(d}%t|%d)|$td*t|#| |#|$|%}#td+t|#d,d-|#D}&|d.krH||j4d/}'|'5|||&|j6W5QRXn,||j4d/}'|'5| ||&|j6W5QRXnt7d0|j+| 8d |ksd|kr|rtj!|\}}(|j9||td1|td2|ntd2|j)dS)3Nr) check_contexta------------------------------------------------------------------------------------------- preprocess txt2mindrecord using {}. ['vocab_4w' / 'vocab_13w' / 'your_vocab_file_path']. # 'vocab_4w' for [alpha and evolution], # 'vocab_13w' for [mPangu], # or manual setting 'your_vocab_file_path', which will using JIEBATokenizer! ----------------------------------------------------------------------------------------- mindsporezi> txt2bin only support 'mindspore' backend, U need to set pcl_pangu.context.set_context backend correctly) tokenizermodelarts_flagmoxargsJIEBATokenizer FileWritertokenize_lambada tokenize_wikidivideIntoNstrand task_unittask_unit_mPangu package_filesetup_tokenizer setup_writer)PoolzL> You need to set input_glob[str] correctly! absolute path is recommended...zO> You need to set output_prefix[str] correctly! absolute path is recommended...zL> You need to set vocab_file[str] correctly! absolute path is recommended...zs3:zobs:zB> OBS findError: cannot find *.txt in your input_glob, check againz> Copy *.txt from OBSz./cache/dataset//z(#### Moxing copy dataset succsseed! ####z./cache/dataset/*zo> 1. check you input_glob path if start with ['s3:'] or ['obs:'],> 2. check you container env if in [modelarts]z./cache/bpe_vocab/z.vocabz vocab.vocabz.modelz vocab.modelz,#### Moxing copy vocab files succsseed! ####z./cache/bpe_vocab/vocabzo> 1. check you vocab_file path if start with ['s3:'] or ['obs:'],> 2. check you container env if in [modelarts]zpad id :zeot id :z vocab size :z./cache/dataMindrecord/ZwikizTransformed {} records.ZlambadaZ openwebtext ofz of znum files of cluster : znum files of this machine : css|] }|VqdS)N).0xrrQD:\并行平台\codes\AISyn_develop\pcl_pangu\pcl_pangu\dataset\txt2mindrecord.py |sz!txt2mindrecord.. vocab_13w)Z processeszNot support dataset type: {}z<#### Moxing upload output mindrecord to [{}] succsseed! ####z*Transform finished, output files refer: {}):pcl_pangu.contextrprintformat ImportErrorZpre_process_chineserrrr r r r r rrrrrrglobrandommultiprocessingr isinstancestrfilelenAssertionErrorospathexistsmakedirssplitcopy input_glob vocab_fileZpad_idZeot_id vocab_sizeabspath output_dirmkdirZ dataset_typeZwrite_raw_datalistiglobsortseedshuffleintZ rankOfClusterZ num_processmapZfile_batch_size ValueErrorZcommitZ copy_parallel))r2 output_prefixr3rBACKENDrrr r r r r rrrrrrr$r%rZinput_txt_from_obs_globZcopy_diritem file_nameZPADZEOTZupload2obs_dirZupload2obs_file_nameZoutput_mindrecord_dirZout_dirout_filewriterZtransforms_countrZ input_filesallorder file_iterpool_rrrtxt2mindrecords @                       rK__main__r)r2r@)r)r,r-dirnamer5__file__ current_dirrK__name__rrrrs  pcl_pangu/pcl_pangu/dataset/pre_process_chinese.py000066400000000000000000000343271443434453700230430ustar00rootroot00000000000000# Copyright 2020 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """ transform wikitext-2, wikitext-103, lambada, openwebtext dataset to mindrecord. """ import argparse import json import os import re import sys import random import numpy as np sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir))) current_dir = os.path.dirname(os.path.abspath(__file__)) sys.path.append(os.path.join(current_dir, '../model/panguAlpha_mindspore')) from pcl_pangu.model.panguAlpha_mindspore.src.tokenization_jieba import JIEBATokenizer from pcl_pangu.tokenizer import vocab_4w, vocab_13w try: import moxing as mox modelarts_flag = True except: ImportWarning("> Not using [PCL-yunnao-modelarts] resources! Are u using local NPU-machine?") modelarts_flag = False from mindspore.mindrecord import FileWriter from multiprocessing import current_process tokenizer = None writer = None parser = argparse.ArgumentParser() parser.add_argument('--dataset_type', type=str, default='openwebtext') parser.add_argument('--data_url', type=str, default='openwebtext') parser.add_argument('--train_url', type=str, default='openwebtext') ########################### parser.add_argument('--input_glob', type=str, default='/userhome/dataset/chinese_txt/webtext2019zh/*.txt') ############################# parser.add_argument('--output_dir', type=str, default='/cache/dataMindrecord/PD_NewPET_0704_mindrecord') parser.add_argument('--file_partition', type=int, default=1) parser.add_argument('--file_batch_size', type=int, default=1) #不影响结果 parser.add_argument('--num_process', type=int, default=200) parser.add_argument('--tokenizer', type=str, default='vocab_4w') parser.add_argument('--SEQ_LEN', type=int, default=1025) parser.add_argument('--rankOfCluster', type=str, default='0of1') args = parser.parse_args() SEQ_LEN = args.SEQ_LEN # the length of sample # EOT = 50256 # id of endoftext # tokenizer = GPT2Tokenizer.from_pretrained("gpt2") def setup_tokenizer(): global tokenizer if args.tokenizer == 'vocab_4w': vocab_path = vocab_4w + '.vocab' model_file = vocab_4w + '.model' tokenizer = JIEBATokenizer(vocab_path, model_file) elif args.tokenizer == 'vocab_13w': from pcl_pangu.tokenizer.spm_13w.tokenizer import SpmTokenizer, langs_ID, translate_ID vocab_file = vocab_13w tokenizer = SpmTokenizer(vocab_file) else: vocab_path = vocab_4w + '.vocab' model_file = vocab_4w + '.model' tokenizer = JIEBATokenizer(vocab_path, model_file) return tokenizer def setup_writer(args): global writer schema = {"input_ids": {"type": "int32", "shape": [-1]}, } writer = FileWriter(file_name=args.output_dir, shard_num=args.file_partition) writer.add_schema(schema, args.dataset_type) writer.open_and_set_header() return writer def chunks(lst, n): """ yield n sized chunks from list""" for i in range(0, len(lst), n): yield lst[i:i+n] # 将一个list尽量均分成n份，限制len(list)==n，份数大于原list内元素个数则分配空list[] def divideIntoNstrand(listTemp, n): twoList = [ [] for i in range(n)] for i,e in enumerate(listTemp): twoList[i%n].append(e) return twoList def package_file(it, n): """ package multiple files""" stop = False while not stop: batch = [] for _ in range(n): try: batch.append(next(it)) except StopIteration: stop = True if not batch: break yield batch def clean_wikitext(string): """ cleaning wikitext dataset""" # contractions string = string.replace("s '", "s'") string = re.sub(r"/' [0-9]/", r"/'[0-9]/", string) # number separators string = string.replace(" @-@ ", "-") string = string.replace(" @,@ ", ",") string = string.replace(" @.@ ", ".") # punctuation string = string.replace(" : ", ": ") string = string.replace(" ; ", "; ") string = string.replace(" . ", ". ") string = string.replace(" ! ", "! ") string = string.replace(" ? ", "? ") string = string.replace(" , ", ", ") # double brackets string = re.sub(r"\(\s*([^\)]*?)\s*\)", r"(\1)", string) string = re.sub(r"\[\s*([^\]]*?)\s*\]", r"[\1]", string) string = re.sub(r"{\s*([^}]*?)\s*}", r"{\1}", string) string = re.sub(r"\"\s*([^\"]*?)\s*\"", r'"\1"', string) string = re.sub(r"'\s*([^']*?)\s*'", r"'\1'", string) # miscellaneous string = string.replace("= = = =", "====") string = string.replace("= = =", "===") string = string.replace("= =", "==") string = string.replace(" "+chr(176)+" ", chr(176)) string = string.replace(" \n", "\n") string = string.replace("\n ", "\n") string = string.replace(" N ", " 1 ") string = string.replace(" 's", "'s") return string def padding_eot(chunk): pad = [tokenizer.pad_id] * (SEQ_LEN - len(chunk)) chunk.extend(pad) return chunk def tokenize_openwebtext(iterator): """ tokenize openwebtext dataset""" content = [] for file_path in iterator: if os.path.getsize(file_path) == 0: continue content = [] with open(file_path, 'r', encoding='utf-8') as f: for para in f.read().split("\n\n"): if para: ############################################### tokenized_text = tokenizer.tokenize(para) content += tokenizer.convert_tokens_to_ids(tokenized_text) + [tokenizer.eot_id] ########################################### for chunk in chunks(content, SEQ_LEN): sample = {} if len(chunk) == SEQ_LEN: sample['input_ids'] = np.array(chunk, dtype=np.int32) yield sample else: sample['input_ids'] = np.array(padding_eot(chunk), dtype=np.int32) yield sample def tokenize_openwebtext_mpangu(iterator): """ tokenize openwebtext dataset""" from pcl_pangu.tokenizer.spm_13w.tokenizer import langs_ID, translate_ID for file_path in iterator: if os.path.getsize(file_path) == 0: continue content = [] with open(file_path, 'r', encoding='utf-8') as f: for para in f.read().split("\n\n"): if para: ############################################### ##----------------------单语语料处理-------------------- if 'corpus' not in file_path.split('/')[-1]: langs = file_path.split('/')[-1][:2] id_langs = langs_ID[langs] para = '' + para tokenized_text_langs = tokenizer.tokenize(para) langs_id = tokenizer.convert_tokens_to_ids(tokenized_text_langs) content.append([id_langs] + langs_id + [tokenizer.eot_id]) else: ##----------------双语语料处理------------------------------ src_langs = file_path.split('/')[-1][:2] tag_langs = file_path.split('/')[-1][3:5] try: src_data, tag_data = para.split("\t") # pangu 4w词表中文文本需要jiaba分词，spm中文文本不需要jieba分词 src_data_new = '' + src_data tag_data_new = '' + tag_data # zh corpus save ,other corpus save mono random if isinstance(src_data_new, str) and isinstance(tag_data_new, str): tokenized_text_src = tokenizer.tokenize(src_data_new) src_id = tokenizer.convert_tokens_to_ids(tokenized_text_src) tokenized_text_tag = tokenizer.tokenize(tag_data_new) tag_id = tokenizer.convert_tokens_to_ids(tokenized_text_tag) content.append([langs_ID[src_langs]] + \ src_id + \ [translate_ID] + \ [langs_ID[tag_langs]] + \ tag_id + [tokenizer.eot_id]) content.append([langs_ID[tag_langs]] + \ tag_id + \ [translate_ID] + \ [langs_ID[src_langs]] + \ src_id + [tokenizer.eot_id]) else: print("Not 2 para str...\n") except: print("Split error, jump...", para) ########################################### random.shuffle(content) content_new = [] for i in content: content_new += i for chunk in chunks(content, SEQ_LEN): sample = {} if len(chunk) == SEQ_LEN: sample['input_ids'] = np.array(chunk, dtype=np.int32) yield sample else: sample['input_ids'] = np.array(padding_eot(chunk), dtype=np.int32) yield sample def tokenize_openwebtext_padEachPara(iterator): """ tokenize openwebtext dataset""" content = [] for file_path in iterator: if os.path.getsize(file_path) == 0: continue with open(file_path, 'r', encoding='utf-8') as f: for para in f.read().split("\n\n"): if para: content = [] ############################################### tokenized_text = tokenizer.tokenize(para) content += tokenizer.convert_tokens_to_ids(tokenized_text) + [tokenizer.eot_id] ########################################### for chunk in chunks(content, SEQ_LEN): sample = {} if len(chunk) == SEQ_LEN: sample['input_ids'] = np.array(chunk, dtype=np.int32) yield sample else: sample['input_ids'] = np.array(padding_eot(chunk), dtype=np.int32) yield sample def tokenize_wiki(file_path): """tokenize wikitext-2/wikitext-103 dataset""" content = [] with open(file_path, 'r', encoding='utf-8') as f: for para in clean_wikitext(f.read()).split("\n\n"): if para and para.strip().startswith('=') is False: tokenized_text = tokenizer.tokenize(para) content += tokenizer.convert_tokens_to_ids(tokenized_text) + [ tokenizer.eot_id] for chunk in chunks(content, SEQ_LEN): sample = {} if len(chunk) == SEQ_LEN: sample['input_ids'] = np.array(chunk, dtype=np.int32) yield sample def tokenize_lambada(file_path): """tokenize lambada dataset""" content = [] with open(file_path, 'r', encoding='utf-8') as f: for line in f.readlines(): para = json.loads(line)['text'].replace( "“", '""').replace("”", '"').strip().strip(".") tokenized_text = tokenizer.tokenize(para) content += tokenizer.convert_tokens_to_ids(tokenized_text) + [tokenizer.eot_id] for chunk in chunks(content, SEQ_LEN): sample = {} if len(chunk) == SEQ_LEN: sample['input_ids'] = np.array(chunk, dtype=np.int32) yield sample def conver_words_to_ids_ch(words, word2id, numchword): ids = [] for word in words: if word in word2id: ids.append(word2id[word]) return ids def task_unit(iterator, parallel_writer=True): """task for each process""" p = current_process() index = p.pid if p.pid else 0 item_iter = tokenize_openwebtext(iterator) batch_size = 1024 # size of write batch count = 0 while True: data_batch = [] try: for _ in range(batch_size): data_batch.append(next(item_iter)) count += 1 writer.write_raw_data(data_batch, parallel_writer=parallel_writer) print("Process {} transformed {} records.".format( index, count)) except StopIteration: if data_batch: writer.write_raw_data(data_batch, parallel_writer=parallel_writer) print("Process {} transformed {} records.".format( index, count)) break def task_unit_mPangu(iterator, parallel_writer=True): """task for each process""" p = current_process() index = p.pid if p.pid else 0 item_iter = tokenize_openwebtext_mpangu(iterator) batch_size = 1024 # size of write batch count = 0 while True: data_batch = [] try: for _ in range(batch_size): data_batch.append(next(item_iter)) count += 1 writer.write_raw_data(data_batch, parallel_writer=parallel_writer) print("Process {} transformed {} records.".format( index, count)) except StopIteration: if data_batch: writer.write_raw_data(data_batch, parallel_writer=parallel_writer) print("Process {} transformed {} records.".format( index, count)) breakpcl_pangu/pcl_pangu/dataset/preprocess_data_pangu.py000066400000000000000000000117741443434453700233720ustar00rootroot00000000000000# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Processing data for pretraining.""" import os import sys sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir))) current_dir = os.path.dirname(os.path.abspath(__file__)) sys.path.append(os.path.join(current_dir, '../model/panguAlpha_pytorch')) import argparse import multiprocessing import glob from megatron.tokenizer.tokenization_jieba import JIEBATokenizer from pcl_pangu.tokenizer import vocab_4w import time import torch try: import nltk nltk_available = True except ImportError: nltk_available = False from megatron.data import indexed_dataset class IdentitySplitter(object): def tokenize(self, *text): return text class Encoder(object): def __init__(self, args): self.args = args if self.args.vocab_file == vocab_4w: self.tokenizer = JIEBATokenizer(self.args.vocab_file) else: print("> We recommend you using JIEBATokenizer to build your vocab ") self.tokenizer = JIEBATokenizer(self.args.vocab_file) def encode(self, iterator): key = self.args.json_keys[0] len_paras = 0 ids = {} doc_ids = [] encode_start_time = time.time() file_num = 0 for file_path in iterator: print(file_path) each_start_time = time.time() json_line = open(file_path, 'r', encoding='utf-8') strr = json_line.read() lista = strr.split('\n\n') len_paras += len(lista) for para in lista: if para: contenta = self.tokenizer.tokenize(para) para_ids = self.tokenizer.convert_tokens_to_ids(contenta) if len(para_ids) > 0: doc_ids.append(para_ids) if self.args.append_eod: for i in range(self.args.eod_num): doc_ids[-1].append(self.tokenizer.eod_id) # print(doc_ids) each_end_time = time.time() print("encode this file using {}s".format(each_end_time - each_start_time)) ids[key] = doc_ids encode_end_time = time.time() print("FINISHING ENCODING, USING {}s".format(encode_end_time - encode_start_time)) return ids, len_paras # print('len_paras',len_paras) def package_file(it, n): """ package multiple files""" stop = False while not stop: batch = [] for _ in range(n): try: batch.append(next(it)) except StopIteration: stop = True if not batch: break yield batch def get_args(): parser = argparse.ArgumentParser() group = parser.add_argument_group(title='input data') group.add_argument('--input', type=str, default='/raid/gpt3-train-data/data-v1/new2016zh/txt-data/train/0000*.txt', help='Path to input txt') group.add_argument('--json-keys', nargs='+', default=['text'], help='space separate listed of keys to extract from json') group.add_argument('--split-sentences', action='store_true', help='Split documents into sentences.') group = parser.add_argument_group(title='tokenizer') group.add_argument('--vocab-file', type=str, default='tokenizer/bpe_4w_pcl/vocab', help='Path to the vocab file') group.add_argument('--append-eod', action='store_true', default=True, help='Append an token to the end of a document.') group.add_argument('--eod-num', type=int, default=1, help='eot number.') group = parser.add_argument_group(title='output data') group.add_argument('--output-prefix', type=str, default="", help='Path to binary output file without suffix') group.add_argument('--dataset-impl', type=str, default='mmap', choices=['lazy', 'cached', 'mmap']) group = parser.add_argument_group(title='runtime') group.add_argument('--workers', type=int, default=200, help='Number of worker processes to launch') group.add_argument('--log-interval', type=int, default=1, help='Interval between progress updates') args = parser.parse_args() args.keep_empty = False return argspcl_pangu/pcl_pangu/dataset/txt2bin.py000066400000000000000000000071161443434453700204070ustar00rootroot00000000000000import time import sys from pcl_pangu.tokenizer import vocab_4w def txt2bin(input_glob, output_prefix, vocab_file="vocab_4w"): from pcl_pangu.context import check_context BACKEND = check_context() if not BACKEND == 'pytorch': raise ImportError("> txt2bin only support 'pytorch' backend, " "U need to set pcl_pangu.context.set_context backend correctly") import torch from .preprocess_data_pangu import get_args, nltk_available, Encoder, package_file, indexed_dataset import glob import nltk import multiprocessing args = get_args() startup_start = time.time() if not isinstance(input_glob, str) or input_glob == "": raise ImportError("> You need to set input_glob[str] correctly! absolute path is recommended...") if not isinstance(output_prefix, str) or output_prefix == "": raise ImportError("> You need to set output_prefix[str] correctly! absolute path is recommended...") if not isinstance(vocab_file, str) or vocab_file == "": raise ImportError("> You need to set vocab_file[str] correctly! absolute path is recommended...") if vocab_file == 'vocab_4w': args.vocab_file = vocab_4w else: args.vocab_file = vocab_file args.input = input_glob args.output_prefix = output_prefix print("Opening", args.input) file_iter = glob.iglob(args.input) if nltk_available and args.split_sentences: nltk.download("punkt", quiet=True) encoder = Encoder(args) # tokenizer = JIEBATokenizer(vocab_path, tokenizer_path) pool = multiprocessing.Pool(args.workers) encoded_docs = pool.imap(encoder.encode, package_file(file_iter, 128)) # , all_lens)) # encoded_docs = map(encoder.encode, fin) print('encoded_docs', encoded_docs) level = "document" if args.split_sentences: level = "sentence" # print(f"Vocab size: {tokenizer.vocab_size}") print(f"Output prefix: {args.output_prefix}") output_bin_files = {} output_idx_files = {} builders = {} for key in args.json_keys: output_bin_files[key] = "{}{}_{}.bin".format(args.output_prefix, key, level) output_idx_files[key] = "{}{}_{}.idx".format(args.output_prefix, key, level) builders[key] = indexed_dataset.make_builder(output_bin_files[key], impl=args.dataset_impl, vocab_size=encoder.tokenizer.vocab_size) startup_end = time.time() proc_start = time.time() total_bytes_processed = 0 print("Time to startup:", startup_end - startup_start) for i, (doc, bytes_processed) in enumerate(encoded_docs, start=1): total_bytes_processed += bytes_processed for key, sentences in doc.items(): for sentence in sentences: builders[key].add_item(torch.IntTensor(sentence)) builders[key].end_document() if i % args.log_interval == 0: current = time.time() elapsed = current - proc_start mbs = total_bytes_processed / elapsed / 1024 / 1024 print(f"Processed {i} documents", f"({i / elapsed} docs/s, {mbs} MB/s).", file=sys.stderr) for key in args.json_keys: builders[key].finalize(output_idx_files[key]) end_time = time.time() print('Preprocess data using {}s'.format(end_time - startup_end)) if __name__ == '__main__': txt2bin(input_glob="", output_prefix="", vocab_file="")pcl_pangu/pcl_pangu/dataset/txt2mindrecord.py000066400000000000000000000155041443434453700217650ustar00rootroot00000000000000import logging import os current_dir = os.path.dirname(os.path.abspath(__file__)) def txt2mindrecord(input_glob, output_prefix, vocab_file='vocab_4w'): from pcl_pangu.context import check_context BACKEND = check_context() print("-------------------------------------------------------------------------------------------" "\n preprocess txt2mindrecord using {}. ['vocab_4w' / 'vocab_13w' / 'your_vocab_file_path']." "\n # 'vocab_4w' for [alpha and evolution], " "\n # 'vocab_13w' for [mPangu]," "\n # or manual setting 'your_vocab_file_path', which will using JIEBATokenizer!" "\n-----------------------------------------------------------------------------------------" .format(vocab_file)) if not BACKEND == 'mindspore': raise ImportError("> txt2bin only support 'mindspore' backend, " "U need to set pcl_pangu.context.set_context backend correctly") from .pre_process_chinese import tokenizer, modelarts_flag, args, JIEBATokenizer, \ FileWriter, tokenize_lambada, tokenize_wiki, divideIntoNstrand, task_unit, task_unit_mPangu, \ package_file, setup_tokenizer, setup_writer try: import moxing as mox except: logging.INFO('> Using NPU Machine, not in modelarts ENV !!') import glob import random from multiprocessing import Pool global tokenizer if not isinstance(input_glob, str) or input_glob == "": raise ImportError("> You need to set input_glob[str] correctly! absolute path is recommended...") if not isinstance(output_prefix, str) or output_prefix == "": raise ImportError("> You need to set output_prefix[str] correctly! absolute path is recommended...") if not isinstance(vocab_file, str) or vocab_file == "": raise ImportError("> You need to set vocab_file[str] correctly! absolute path is recommended...") if "s3:" in input_glob or "obs:" in input_glob: input_txt_from_obs_glob = mox.file.glob(input_glob) assert not len(input_txt_from_obs_glob) == 0, "> OBS findError: cannot find *.txt in your input_glob" \ ", check again" print('> Copy *.txt from OBS') if modelarts_flag: copy_dir = './cache/dataset/' if not os.path.exists(copy_dir): os.makedirs(copy_dir) for item in input_txt_from_obs_glob: file_name = item.split('/')[-1] mox.file.copy(item, copy_dir + file_name) print('#### Moxing copy dataset succsseed! ####') args.input_glob = './cache/dataset/*' else: raise ImportError("> 1. check you input_glob path if start with ['s3:'] or ['obs:']," "> 2. check you container env if in [modelarts]") else: args.input_glob = input_glob if "s3:" in vocab_file or "obs:" in vocab_file: if modelarts_flag: copy_dir = './cache/bpe_vocab/' if not os.path.exists(copy_dir): os.makedirs(copy_dir) mox.file.copy(vocab_file + '.vocab', copy_dir + 'vocab.vocab') mox.file.copy(vocab_file + '.model', copy_dir + 'vocab.model') print('#### Moxing copy vocab files succsseed! ####') vocab_file = './cache/bpe_vocab/vocab' args.vocab_file = vocab_file else: raise ImportError("> 1. check you vocab_file path if start with ['s3:'] or ['obs:']," "> 2. check you container env if in [modelarts]") else: args.vocab_file = vocab_file ####################################### tokenizer = setup_tokenizer() ####################################### PAD = tokenizer.pad_id EOT = tokenizer.eot_id print('pad id :', PAD) print('eot id :', EOT) print('vocab size :', tokenizer.vocab_size) if "s3:" in output_prefix or "obs:" in output_prefix: if modelarts_flag: upload2obs_dir, upload2obs_file_name = os.path.split(os.path.abspath(output_prefix)) output_mindrecord_dir = './cache/dataMindrecord/' if not os.path.exists(output_mindrecord_dir): os.makedirs(output_mindrecord_dir) args.output_dir = output_mindrecord_dir + upload2obs_file_name else: raise ImportError("> 1. check you input_glob path if start with ['s3:'] or ['obs:']," "> 2. check you container env if in [modelarts]") else: args.output_dir = output_prefix out_dir, out_file = os.path.split(os.path.abspath(args.output_dir)) if not os.path.exists(out_dir): os.mkdir(out_dir) writer = setup_writer(args) ### transforms_count = 0 if args.dataset_type == 'wiki': for x in tokenize_wiki(args.input_glob): transforms_count += 1 writer.write_raw_data([x]) print("Transformed {} records.".format(transforms_count)) elif args.dataset_type == 'lambada': for x in tokenize_lambada(args.input_glob): transforms_count += 1 writer.write_raw_data([x]) print("Transformed {} records.".format(transforms_count)) elif args.dataset_type == 'openwebtext': # file_iter = glob.iglob(args.input_glob) input_files = list(glob.iglob(args.input_glob)) # input_files = input_files*2 input_files.sort() random.seed(10) random.shuffle(input_files) all = int(args.rankOfCluster.split('of')[1]) order = int(args.rankOfCluster.split('of')[0]) print(order, ' of ', all) print('num files of cluster : ', len(input_files)) input_files = divideIntoNstrand(input_files, all)[order] print('num files of this machine : ', len(input_files)) file_iter = (x for x in input_files) if vocab_file == 'vocab_13w': with Pool(processes=args.num_process) as pool: pool.map(task_unit_mPangu, package_file(file_iter, args.file_batch_size)) else: with Pool(processes=args.num_process) as pool: pool.map(task_unit, package_file(file_iter, args.file_batch_size)) else: raise ValueError( "Not support dataset type: {}".format(args.dataset_type)) writer.commit() if "s3:" in output_prefix or "obs:" in output_prefix: if modelarts_flag: upload2obs_dir, _ = os.path.split(output_prefix) mox.file.copy_parallel(out_dir, upload2obs_dir) print('#### Moxing upload output mindrecord to [{}] succsseed! ####'.format(upload2obs_dir)) print("Transform finished, output files refer: {}".format(output_prefix)) else: print("Transform finished, output files refer: {}".format(args.output_dir)) if __name__ == '__main__': txt2mindrecord(input_glob="", output_prefix="") pcl_pangu/pcl_pangu/model/000077500000000000000000000000001443434453700161115ustar00rootroot00000000000000pcl_pangu/pcl_pangu/model/__init__.py000066400000000000000000000001721443434453700202220ustar00rootroot00000000000000from . import alpha from . import evolution from . import mPangu __all__ = [ "alpha", "evolution", "mPangu" ]pcl_pangu/pcl_pangu/model/__pycache__/000077500000000000000000000000001443434453700203215ustar00rootroot00000000000000pcl_pangu/pcl_pangu/model/__pycache__/__init__.cpython-38.pyc000066400000000000000000000004541443434453700245130ustar00rootroot00000000000000U b@s2ddlmZddlmZddlmZdddgZdS))alpha) evolution)mPangurrrN)rrr__all__rrID:\并行平台\codes\AISyn_develop\pcl_pangu\pcl_pangu\model\__init__.pys   pcl_pangu/pcl_pangu/model/__pycache__/launcher_torch.cpython-38.pyc000066400000000000000000000035131443434453700257530ustar00rootroot00000000000000U b @sTddlZddlZddlZddlmZddddddddgdf ddZed krPedS) N)loggeris  Apcl_pangu/pcl_pangu/model/alpha/000077500000000000000000000000001443434453700171765ustar00rootroot00000000000000pcl_pangu/pcl_pangu/model/alpha/__init__.py000066400000000000000000000003671443434453700213150ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2022/7/20 # @Author: 2022 PCL from .alpha import train, fine_tune, inference from .config_alpha import model_config_gpu, \ model_config_npu, \ model_config_onnx, \ MODEL_CONFIG pcl_pangu/pcl_pangu/model/alpha/__pycache__/000077500000000000000000000000001443434453700214065ustar00rootroot00000000000000pcl_pangu/pcl_pangu/model/alpha/__pycache__/__init__.cpython-38.pyc000066400000000000000000000005101443434453700255710ustar00rootroot00000000000000U b@s(ddlmZmZmZddlmZmZdS))train fine_tune inference)model_config_gpumodel_config_npuN)alpharrrZ config_alpharrrrOD:\并行平台\codes\AISyn_develop\pcl_pangu\pcl_pangu\model\alpha\__init__.pyspcl_pangu/pcl_pangu/model/alpha/__pycache__/alpha.cpython-38.pyc000066400000000000000000000077261443434453700251370ustar00rootroot00000000000000U $Jb@sddlZddlZddlmZddlmZddlmZddlm Z m Z m Z dd Z d d Z dddZddZddZddZedkre ZeedS)N)logger)launch) check_context)DISTRUBUTED_CONFIGmodel_config_gpumodel_config_npucCstdtdtd|jtd|jtd|jtd|jtdtdkrt|tspt | }d }t ||n(td krt|t st | }t|dS) NzF---------------------------- train config ----------------------------> Base Model: [alpha]> Model Size: [{}]> data_path: {}> global batch_size: {}> save to path: {}F--------------------------- end of config ----------------------------pytorch$/panguAlpha_pytorch/pretrain_gpt2.py mindspore)printformatmodel data_path batch_sizesaver isinstancerAssertionError_get_training_script_argsrun_ptr _cover_modelarts_training_args run_ms_train)config script_args py_script config_dictr#LD:\并行平台\codes\AISyn_develop\pcl_pangu\pcl_pangu\model\alpha\alpha.pytrains    r%cCstdtdtd|jtd|jtd|jtd|jtdtdkrd d lm}d |d <| }d }t ||n(tdkrt |t st |}t|dS)NzF-------------------------- finetune config ---------------------------r r r r rrrrDEFAULT_CONFIGTfinetunerr)rrrrrrr config_alphar'rrrr rrr)rr'r r!r"r#r#r$ fine_tune$s"    r*?Tc Csntdtdtd|jtd|jtd|jtd|jtdtdkrd d lm}d |d <d |_|j |d }d} | d|| d||dk r| d| d |dk r| d||dk r| d|t || n\tdkrjt|ts*t|} || d<|| d<|| d<|| d<|| d<|} t| dS)NzG--------------------------- inference config --------------------------r r r r rzG---------------------------- end of config ----------------------------rrr&Tr()oneCardInferencez3/panguAlpha_pytorch/tools/generate_samples_Pangu.pyz --top-k={}z --top-p={}z--sample-input={}zutf-8z--sample-input-file={}z--sample-output-file={}rtop_ktop_pinput input_file output_file)rrrrrrrr)r'rappendencodehexrrr rrrun_ms_inference) rr-r.r/r0r1r,r'r r!r"r#r#r$ inference9s@      r6cKsXtjtjt}tj|d||}td||t f||dt 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train_iterslr_decay_iterssaveload data_path vocab_file merge_file data_implsplitdistributed_backendlrlr_decay_stylemin_lr weight_decay clip_gradwarmupcheckpoint_activations log_interval save_interval eval_interval eval_itersfp16finetunetokenizer_typec@sHeZdZddddddedddddd d fd d Zed dZdddZdS)model_config_gpurrri'rdataNrrrcCs||_||_||_||_td|j|_||_||_|dk rR| dkrJ|jn| |_n| |_||_ ||_ | t d<| t d<| t d<| t d<|t d<dS)Ng{Gz?rrrrr) modelr r%r&intr'r0r)r(r*r+DISTRUBUTED_CONFIG)selfr@r r%r&r0r*r+r)r(rrrrrrDSD:\并行平台\codes\AISyn_develop\pcl_pangu\pcl_pangu\model\alpha\config_alpha.py__init__Ms"zmodel_config_gpu.__init__c Csjg}|D]X\}}|dkrq |dkr>|d|ddq |dkrJq q |d|dd|q |S)Nr@Tz--_-Fz--{}={})itemsappendreplaceformat) config_dictcmdkvrDrDrE _dict_to_cmdoszmodel_config_gpu._dict_to_cmdFcCs|rdt|jd<tt|jt}t|}|j|d<|j|d<|j|d<|j |d<|j |d<| D]\}}|||<qh| |}|S)Nrr r)r(r*r+r') MODEL_CONFIGr@copydeepcopyDEFAULT_CONFIGvarsr)r(r*r+r'rIrQ)rConeCardInferencedefault_config_varsrOrPrNrDrDrE_get_training_script_args~s       z*model_config_gpu._get_training_script_args)F)__name__ __module__ __qualname__rrF staticmethodrQrZrDrDrDrEr>Ls$ " r>c@s<eZdZddddddddedd d d d fd d Zdd dZd S)model_config_npurrrr!gư>ir?i@NFcCs||_||_||_||_||_||_||_| |_||_| dk rT| dkrL|jn| |_ n| |_ ||_ | |_ | |_ | |_ |jr|jdk stddS)Nz/> Please set your pretrained [model.ckpt] path!)r@r r%train_epoch_sizer'start_lrend_lrr)r<r(r*r+ vocab_sizestrategy_load_ckpt_pathAssertionError)rCr@r r%r`rarbr'r*r+rcr)r(rdr<rDrDrErFs$zmodel_config_npu.__init__cCs|rdt|jd<tt|jt}t|}|j|d<|j|d<|j|d<|j |d<|j |d<|j |d<|j |d <| D]\}}|||<q||S) Nrr r)r(r<r*r+rcr')rRr@rSrTrUrVr)r(r<r*r+rcr'rI)rCrWrXrYrOrPrDrDrE_cover_modelarts_training_argss        z/model_config_npu._cover_modelarts_training_args)F)r[r\r]rrFrfrDrDrDrEr_s  &r___main__r) rSpcl_pangu.tokenizerrrRrB vocab_dirrUr>r_r[printrDrDrDrEsz   D9pcl_pangu/pcl_pangu/model/alpha/alpha.py000066400000000000000000000257471443434453700206540ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2022/7/20 # @Author: 2022 PCL import os import sys from loguru import logger from ..launcher_torch import launch from pcl_pangu.context import check_context from .config_alpha import DISTRUBUTED_CONFIG, model_config_gpu, model_config_npu def train(config): print('---------------------------- train config ----------------------------') print("> Base Model: [alpha]") print("> Model Size: [{}]".format(config.model)) print("> data_path: {}".format(config.data_path)) print("> global batch_size: {}".format(config.batch_size)) print("> save to path: {}".format(config.save)) print('--------------------------- end of config ----------------------------') if check_context()=='pytorch': assert isinstance(config, model_config_gpu) script_args = config._get_training_script_args() py_script = '/panguAlpha_pytorch/pretrain_gpt2.py' run_pt(script_args, py_script) elif check_context()=='mindspore': assert isinstance(config, model_config_npu) config_dict = config._cover_modelarts_training_args() run_ms_train(config_dict) def fine_tune(config): print('-------------------------- finetune config ---------------------------') print("> Base Model: [alpha]") print("> Model Size: [{}]".format(config.model)) print("> data_path: {}".format(config.data_path)) print("> global batch_size: {}".format(config.batch_size)) print("> save to path: {}".format(config.save)) print('--------------------------- end of config ----------------------------') if check_context()=='pytorch': from .config_alpha import DEFAULT_CONFIG DEFAULT_CONFIG['finetune'] = True script_args = config._get_training_script_args() py_script = '/panguAlpha_pytorch/pretrain_gpt2.py' run_pt(script_args, py_script) elif check_context()=='mindspore': assert isinstance(config, model_config_npu) config_dict = config._cover_modelarts_training_args() run_ms_train(config_dict) run_ms_finetune_merge_OpenI(config_dict) def inference(config,top_k=1,top_p=0.9,input=None,input_file=None, generate_max_tokens=128, output_file=None,oneCardInference=True): backend_context = check_context() assert generate_max_tokens > 0 and generate_max_tokens <= 800, "> generate_max_tokens always in (0, 800]" print('--------------------------- inference config --------------------------') print("> Base Model: [alpha]") print("> Model Size: [{}]".format(config.model)) print("> global batch_size: {}".format(config.batch_size)) print("> generate_max_tokens length: {}".format(generate_max_tokens)) print('---------------------------- end of config ----------------------------') if backend_context == 'pytorch': from .config_alpha import DEFAULT_CONFIG DEFAULT_CONFIG['finetune'] = True config.batch_size = 1 script_args = config._get_training_script_args(oneCardInference=oneCardInference) py_script = '/panguAlpha_pytorch/tools/generate_samples_Pangu.py' script_args.append('--top-k={}'.format(top_k)) script_args.append('--top-p={}'.format(top_p)) if input is not None: script_args.append('--sample-input={}'.format(input.encode('utf-8').hex())) if input_file is not None: script_args.append('--sample-input-file={}'.format(input_file)) if output_file is not None: script_args.append('--sample-output-file={}'.format(output_file)) if generate_max_tokens is not None: script_args.append('--generate_max_tokens={}'.format(generate_max_tokens)) run_pt(script_args, py_script) elif backend_context == 'mindspore': assert isinstance(config, model_config_npu) config_dict = config._cover_modelarts_training_args(oneCardInference=oneCardInference) config_dict['top_k'] = top_k config_dict['top_p'] = top_p config_dict['input'] = input config_dict['input_file'] = input_file config_dict['output_file'] = output_file config_dict['generate_max_tokens'] = generate_max_tokens run_ms_inference(config_dict) elif 'onnx-' in backend_context: from pcl_pangu.onnx_inference.infer import onnx_generate from pcl_pangu.tokenizer.tokenization_jieba import get_tokenizer num_threads = 2 past_path = None model_path = None for filename in os.listdir(config.load): if filename.endswith('.npy'): past_path = os.path.join(config.load, filename) if filename.endswith('.onnx'): model_path = os.path.join(config.load, filename) tokenizer = get_tokenizer() if input is not None: raw_text = input output_samples = onnx_generate(raw_text, model_path, tokenizer, past_path, topk=top_k, top_p=top_p, threads=num_threads, max_len=generate_max_tokens, backend=backend_context) print('Input is:', raw_text) print('Output is:', output_samples[len(raw_text):], flush=True) print() if input_file is not None: raw_texts = open(input_file, 'r').read().split('\n\n') write_output = print if output_file is not None: output_file = open(output_file, 'w') write_output = lambda x: output_file.write(x + '\n') for raw_text in raw_texts: output_samples = onnx_generate(raw_text, model_path, tokenizer, past_path, topk=top_k, top_p=top_p, threads=num_threads, max_len=generate_max_tokens, backend=backend_context) write_output('Input is: ' + raw_text) write_output('Output is: ' + output_samples[len(raw_text):]) write_output() def run_pt(script_args, py_script, **kwargs): current_dir = os.path.dirname(os.path.dirname(__file__)) sys.path.append(current_dir+'/panguAlpha_pytorch') py_script = current_dir + py_script logger.info("> Running {} with args: {}".format(py_script, script_args)) launch(training_script=py_script, training_script_args=script_args, **DISTRUBUTED_CONFIG, **kwargs) def run_ms_train(config_dict): current_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)) sys.path.append(current_dir + '/panguAlpha_mindspore') from train_alpha_ms13 import opt, setup_args, main, alpha_add_args new_opt = setup_args(opt, config_dict) new_opt = alpha_add_args(new_opt) main(new_opt) def run_ms_inference(config_dict): current_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)) sys.path.append(current_dir + '/panguAlpha_mindspore') from inference_alpha_ms13 import opt, setup_args, main new_opt = setup_args(opt, config_dict) main(new_opt) def run_ms_finetune_merge_OpenI(config_dict): current_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)) sys.path.append(current_dir + '/panguAlpha_mindspore') from train_alpha_ms13 import opt, setup_args, main, alpha_add_args, find_local_ckpt_maxStepNumber import mindspore as ms import mindspore.communication.management as D import moxing as mox import time D.init() device_num = D.get_group_size() rank_id = D.get_rank() args_opt = setup_args(opt, config_dict) args_opt = alpha_add_args(args_opt) # do merge sample_save_dir_rank = os.path.join(args_opt.save_checkpoint_path, f"rank_0") save_dir_strategy = os.path.join(args_opt.save_checkpoint_path, "strategy") strategy = ms.build_searched_strategy("/cache/strategy.ckpt") mp = args_opt.op_level_model_parallel_num # load mp slice modelWeight if rank_id % 8 == 0: maxStepNumber = find_local_ckpt_maxStepNumber(sample_save_dir_rank) sliced_parameters = [] for i in range(mp): save_dir_rank = os.path.join(args_opt.save_checkpoint_path, f"rank_{i}") print(save_dir_rank) os.system("ls {}".format(save_dir_rank)) save_ckptfile_name = args_opt.ckpt_name_prefix + '_' + str(i) model_prefix = os.path.join(save_dir_rank, "{}-{}_2.ckpt".format(save_ckptfile_name, maxStepNumber)) print(model_prefix) param_dict = ms.load_checkpoint(model_prefix) adam_names = [item for item in param_dict.keys() if 'adam' in item] for item in adam_names: param_dict.pop(item) param_dict.pop("scale_sense") param_dict.pop("global_step") param_dict.pop("current_iterator_step") param_dict.pop("last_overflow_iterator_step") param_dict.pop("epoch_num") param_dict.pop("step_num") sliced_parameters.append(param_dict) merged_parameter = {} for key in sliced_parameters[0].keys(): this_merged_list = [sliced_parameters[0].get(key), sliced_parameters[1].get(key)] this_merged_parameter = ms.merge_sliced_parameter(this_merged_list, strategy) merged_parameter[key] = this_merged_parameter # ops_cast = ops.Cast() param_list_fp16 = [] for (key, value) in merged_parameter.items(): if key == 'embedding_table': key = 'backbone.word_embedding.embedding_table' elif key == 'backbone.embedding.embedding.position_embedding.embedding_table': key = 'backbone.position_embedding.embedding_table' elif key == 'backbone.top_query_embedding_table': key = 'backbone.top_query_embedding.embedding_table' each_param_fp16 = {} each_param_fp16["name"] = key if isinstance(value.data, ms.Tensor): param_data_fp16 = ms.Tensor(value.data, ms.float16) # param_data_fp16 = ops_cast(value.data, ms.float16) else: param_data_fp16 = ms.Tensor(value.data, ms.float16) # param_data_fp16 = ops_cast(ms.Tensor(value.data), ms.float16) each_param_fp16["data"] = param_data_fp16 param_list_fp16.append(each_param_fp16) ms.save_checkpoint(param_list_fp16, "/{}/alpha-2B6-merged-{}_2-fp16.ckpt".format(args_opt.save_checkpoint_path, maxStepNumber)) try: mox.file.copy("/{}/alpha-2B6-merged-{}_2-fp16.ckpt".format(args_opt.save_checkpoint_path, maxStepNumber), os.path.join(args_opt.train_url, "alpha-2B6-merged-fp16.ckpt")) except: pass f = open("/cache/merge.txt", 'w') f.close() while not os.path.exists("/cache/merge.txt"): time.sleep(1) print('>>> merge mp={} model-slices to: {} >>>\n'.format(args_opt.op_level_model_parallel_num, args_opt.train_url)) if __name__ == '__main__': config = model_config_gpu() run_pt(config) pcl_pangu/pcl_pangu/model/alpha/config_alpha.py000066400000000000000000000164121443434453700221660ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2022/7/20 # @Author: 2022 PCL import copy from pcl_pangu.tokenizer import vocab_4w MODEL_CONFIG = { '350M': { 'num_layers': 23, 'hidden_size': 1024, 'num_attention_heads': 16, 'seq_length': 1024, 'max_position_embeddings': 1024, 'model_parallel_size': 1, }, '2B6': { 'num_layers': 31, 'hidden_size': 2560, 'num_attention_heads': 32, 'seq_length': 1024, 'max_position_embeddings': 1024, 'model_parallel_size': 2, # 2B6 mp_size >= 2 , recommend for [2,4,8] }, '13B': { 'num_layers': 39, 'hidden_size': 5120, 'num_attention_heads': 40, 'seq_length': 1024, 'max_position_embeddings': 1024, 'model_parallel_size': 8, # 13B mp_size >= 8 , recommend for [8] } } DISTRUBUTED_CONFIG = { 'nnodes':1, 'node_rank':0, 'nproc_per_node':1, 'master_addr':"localhost", 'master_port':29500 } vocab_dir = vocab_4w DEFAULT_CONFIG = { 'model_parallel_size': 1, 'batch_size': 8, 'train_iters': 500000, 'lr_decay_iters': 320000, 'save': '', 'load': '', 'data_path': '', 'vocab_file': vocab_dir, 'merge_file': 'gpt2-merges.txt', 'data_impl': 'mmap', 'split': '949,50,1', 'distributed_backend': 'nccl', 'lr': 0.00015, 'lr_decay_style': 'cosine', 'min_lr': 1.0e-5, 'weight_decay': 1e-2, 'clip_grad': 1.0, 'warmup': 0.01, 'checkpoint_activations': True, 'log_interval': 100, 'save_interval': 1000, 'eval_interval': 1000, 'eval_iters': 10, 'fp16': True, 'finetune': False, 'tokenizer_type':'GPT2BPETokenizer' } class model_config_onnx(): def __init__(self, model='350M',load=None): self.model = model self.load = load self.batch_size = 1 self.model_config = MODEL_CONFIG[model] class model_config_gpu(): def __init__(self, model='350M', model_parallel_size=1, batch_size=8, train_iters=10000, lr=0.00015, data_path='data', vocab_file=vocab_4w, load=None, save=None, nnodes=1, node_rank=0, nproc_per_node=1, master_addr="localhost", master_port=29500): self.model = model self.model_parallel_size = model_parallel_size self.batch_size = batch_size self.train_iters = train_iters self.lr_decay_iters = int(0.64 * self.train_iters) self.lr = lr self.load = load if load is not None: self.save = self.load if save is None else save else: self.save = save self.data_path = data_path self.vocab_file = vocab_file DISTRUBUTED_CONFIG['nnodes'] = nnodes DISTRUBUTED_CONFIG['node_rank'] = node_rank DISTRUBUTED_CONFIG['nproc_per_node'] = nproc_per_node DISTRUBUTED_CONFIG['master_addr'] = master_addr DISTRUBUTED_CONFIG['master_port'] = master_port @staticmethod def _dict_to_cmd(config_dict): cmd = [] for k, v in config_dict.items(): if k == 'model': pass else: if v is True: cmd.append('--' + k.replace('_','-')) elif v is False: continue else: cmd.append('--{}={}'.format(k.replace('_', '-'), v)) return cmd def _get_training_script_args(self, oneCardInference=False): global MODEL_CONFIG, DEFAULT_CONFIG if oneCardInference: tmp_config = MODEL_CONFIG[self.model] tmp_config['model_parallel_size'] = 1 assert self.model_parallel_size == 1, "> mp=1 when Your using OneCardInference!" else: tmp_config = MODEL_CONFIG[self.model] if self.model_parallel_size == 1: self.model_parallel_size = tmp_config['model_parallel_size'] default_config = copy.deepcopy({**tmp_config, **DEFAULT_CONFIG}) _vars = vars(self) default_config['load'] = self.load default_config['save'] = self.save default_config['data_path'] = self.data_path default_config['vocab_file'] = self.vocab_file default_config['lr_decay_iters'] = self.lr_decay_iters # _vars.pop('checkpoint_path') for k, v in _vars.items(): default_config[k] = v cmd = self._dict_to_cmd(default_config) return cmd class model_config_npu(): def __init__(self, model='350M', model_parallel_size=1, batch_size=8, train_iters=50000, save_checkpoint_steps=2000, start_lr=1.0e-5, end_lr=1.0e-6, data_path='data', vocab_file=vocab_4w, vocab_size=40000, load=None, save=None, strategy_load_ckpt_path=None, finetune=False, ): self.model = model self.model_parallel_size = model_parallel_size self.batch_size = batch_size self.train_iters = train_iters self.lr_decay_iters = int(0.64 * self.train_iters) self.save_checkpoint_steps = save_checkpoint_steps self.start_lr = start_lr self.end_lr = end_lr self.load = load self.finetune = finetune if load is not None: self.save = self.load if save is None else save else: self.save = save self.data_path = data_path self.vocab_file = vocab_file self.vocab_size = vocab_size self.strategy_load_ckpt_path = strategy_load_ckpt_path if self.finetune: assert self.load is not None, "> Please set your pretrained [model.ckpt] path!" # assert self.strategy_load_ckpt_path is not None, "> Please set your pretrained model [strategy.ckpt] path!" def _cover_modelarts_training_args(self, oneCardInference=False): global MODEL_CONFIG, DEFAULT_CONFIG if oneCardInference: tmp_config = MODEL_CONFIG[self.model] tmp_config['model_parallel_size'] = 1 assert self.model_parallel_size == 1, "> mp=1 when Your using OneCardInference!" else: tmp_config = MODEL_CONFIG[self.model] if self.model_parallel_size == 1: self.model_parallel_size = tmp_config['model_parallel_size'] default_config = copy.deepcopy({**tmp_config, **DEFAULT_CONFIG}) _vars = vars(self) default_config['load'] = self.load default_config['save'] = self.save default_config['finetune'] = self.finetune default_config['data_path'] = self.data_path default_config['vocab_file'] = self.vocab_file default_config['vocab_size'] = self.vocab_size default_config['lr_decay_iters'] = self.lr_decay_iters default_config['save_checkpoint_steps'] = self.save_checkpoint_steps # _vars.pop('checkpoint_path') for k, v in _vars.items(): default_config[k] = v return default_config if __name__ == '__main__': print(MODEL_CONFIG['350M'])pcl_pangu/pcl_pangu/model/evolution/000077500000000000000000000000001443434453700201355ustar00rootroot00000000000000pcl_pangu/pcl_pangu/model/evolution/__init__.py000066400000000000000000000003131443434453700222430ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2022/7/20 # @Author: 2022 PCL from .evolution import train, fine_tune, inference from .config_evolution import model_config_gpu, model_config_onnxpcl_pangu/pcl_pangu/model/evolution/__pycache__/000077500000000000000000000000001443434453700223455ustar00rootroot00000000000000pcl_pangu/pcl_pangu/model/evolution/__pycache__/__init__.cpython-38.pyc000066400000000000000000000004711443434453700265360ustar00rootroot00000000000000U b@s$ddlmZmZmZddlmZdS))train fine_tune inference)model_config_gpuN) evolutionrrrZconfig_evolutionrrrSD:\并行平台\codes\AISyn_develop\pcl_pangu\pcl_pangu\model\evolution\__init__.pyspcl_pangu/pcl_pangu/model/evolution/__pycache__/config_evolution.cpython-38.pyc000066400000000000000000000053361443434453700303550ustar00rootroot00000000000000U Gb@sddlZddlmZddddddddd d ddd dd adddd ddZeZd ddddddeddddddddddddddd dd!d"d#aGd$d%d%eZdS)&N)vocab_4wi) num_layers hidden_sizenum_attention_heads seq_lengthmax_position_embeddingsmodel_parallel_sizei )350M2B6 localhost=s)nnodes node_ranknproc_per_node master_addr master_porti izgpt2-merges.txtmmapz949,50,1nccla2U0*#?cosinegh㈵>g{Gz?g?Tdi FGPT2BPETokenizer)r batch_size train_iterslr_decay_iterssaveload data_path vocab_file merge_file data_implsplitdistributed_backendlrlr_decay_stylemin_lr weight_decay clip_gradwarmupcheckpoint_activations log_interval save_interval eval_interval eval_itersfp16finetunetokenizer_typec@sHeZdZddddddedddddd d fd d Zed dZdddZdS)model_config_gpurrri'rdataNrrrcCs||_||_||_||_td|j|_||_||_|dk rR| dkrJ|jn| |_n| |_||_ ||_ | t d<| t d<| t d<| t d<|t d<dS)Ng{Gz?rrrrr) modelr r!r"intr#r,r%r$r&r'DISTRUBUTED_CONFIG)selfr<r r!r"r,r&r'r%r$rrrrrr@[D:\并行平台\codes\AISyn_develop\pcl_pangu\pcl_pangu\model\evolution\config_evolution.py__init__Ds"zmodel_config_gpu.__init__c Csjg}|D]X\}}|dkrq |dkr>|d|ddq |dkrJq q |d|dd|q |S)Nr<Tz--_-Fz--{}={})itemsappendreplaceformat) config_dictcmdkvr@r@rA _dict_to_cmdfszmodel_config_gpu._dict_to_cmdFcCs|rdt|jd<tt|jt}t|}|j|d<|j|d<|j|d<|j |d<|j |d<| D]\}}|||<qh| |}|S)Nrr r%r$r&r'r#) MODEL_CONFIGr<copydeepcopyDEFAULT_CONFIGvarsr%r$r&r'r#rErM)r?oneCardInferencedefault_config_varsrKrLrJr@r@rA_get_training_script_argsus       z*model_config_gpu._get_training_script_args)F)__name__ __module__ __qualname__rrB staticmethodrMrVr@r@r@rAr:Cs$ " r:) rOpcl_pangu.tokenizerrrNr> vocab_dirrQobjectr:r@r@r@rAsf  pcl_pangu/pcl_pangu/model/evolution/__pycache__/evolution.cpython-38.pyc000066400000000000000000000060671443434453700270320ustar00rootroot00000000000000U Gb@sddlZddlZddlmZddlmZddlmZddlm Z m Z ddZ dd Z dd dZ ddZedkr|e Ze edS)N)logger) check_context)launch)DISTRUBUTED_CONFIGmodel_config_gpucCstdtdtd|jtd|jtd|jtd|jtdtdkrz|}d }t||n td d SdS) NzK----------------------------- training config -----------------------------> Base Model: [evolution]> Model Size: [{}]> data_path: {}> global batch_size: {}> save to path: {}zJ------------------------------ end of config -----------------------------pytorch)/panguAlpha_pytorch/pretrain_evolution.pyERROR: wrong backend. printformatmodel data_path batch_sizesaver_get_training_script_argsrun_ptconfig script_args py_scriptrTD:\并行平台\codes\AISyn_develop\pcl_pangu\pcl_pangu\model\evolution\evolution.pytrains  rcCstdtdtd|jtd|jtd|jtd|jtdtdkrz|}d }t||n td d SdS) NzI--------------------------- finetune config -----------------------------rrr r r zJ---------------------------- end of config -------------------------------r r rrrrrrr fine_tune!s  r r?Tc Cs(tdtdtd|jtd|jtd|jtd|jtdtdkrd d lm}d |d <d |_|j |d }d} | d|| d||dk r| d| d |dk r| d||dk r| d|| dt || n tdd SdS)NzG--------------------------- inference config --------------------------rrr r r zG---------------------------- end of config ----------------------------r r)DEFAULT_CONFIGTfinetune)oneCardInferencez4/panguAlpha_pytorch/tools/generate_samples_epangu.pyz --top-k={}z --top-p={}z--sample-input={}zutf-8z--sample-input-file={}z--sample-output-file={}z--isEvolution=Truer)rrrrrrrconfig_evolutionr"rappendencodehexr) rtop_ktop_pinput input_file output_filer$r"rrrrr inference3s2      r.cKsXtjtjt}tj|d||}td||t f||dt |dS)Nz/panguAlpha_pytorchz> Running {} with args: {})training_scripttraining_script_argsr) ospathdirname__file__sysr&rinforrr)rrkwargs current_dirrrrrSsr__main__)rr!NNNT)r1r5logururpcl_pangu.contextrZpcl_pangu.model.launcher_torchrZ*pcl_pangu.model.evolution.config_evolutionrrrr r.r__name__rrrrrs    pcl_pangu/pcl_pangu/model/evolution/config_evolution.py000066400000000000000000000105171443434453700240640ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2022/7/20 # @Author: 2022 PCL import copy from pcl_pangu.tokenizer import vocab_4w MODEL_CONFIG = { '350M': { 'num_layers': 23, 'hidden_size': 1024, 'num_attention_heads': 16, 'seq_length': 1024, 'max_position_embeddings': 1024, 'model_parallel_size': 1, }, '2B6': { 'num_layers': 31, 'hidden_size': 2560, 'num_attention_heads': 32, 'seq_length': 1024, 'max_position_embeddings': 1024, 'model_parallel_size': 2, # 2B6 mp_size >= 2 , recommend for [2,4,8] } } DISTRUBUTED_CONFIG = { 'nnodes':1, 'node_rank':0, 'nproc_per_node':1, 'master_addr':"localhost", 'master_port':29501 } vocab_dir = vocab_4w DEFAULT_CONFIG = { 'model_parallel_size': 2, 'batch_size': 8, 'train_iters': 500000, 'lr_decay_iters': 320000, 'save': '', 'load': '', 'data_path': '', 'vocab_file': vocab_dir, 'merge_file': 'gpt2-merges.txt', 'data_impl': 'mmap', 'split': '949,50,1', 'distributed_backend': 'nccl', 'lr': 0.00015, 'lr_decay_style': 'cosine', 'min_lr': 1.0e-5, 'weight_decay': 1e-2, 'clip_grad': 1.0, 'warmup': 0.01, 'checkpoint_activations': True, 'log_interval': 100, 'save_interval': 1000, 'eval_interval': 1000, 'eval_iters': 10, 'fp16': True, 'finetune': False, 'tokenizer_type': 'GPT2BPETokenizer' } class model_config_onnx(): def __init__(self, model='350M',load=None): self.model = model self.load = load self.batch_size = 1 self.model_config = MODEL_CONFIG[model] class model_config_gpu(object): def __init__(self, model='350M', model_parallel_size=1, batch_size=8, train_iters=10000, lr=0.00015, data_path='data', vocab_file=vocab_4w, load=None, save=None, nnodes=1, node_rank=0, nproc_per_node=1, master_addr="localhost", master_port=29501): self.model = model self.model_parallel_size = model_parallel_size self.batch_size = batch_size self.train_iters = train_iters self.lr_decay_iters = int(0.64 * self.train_iters) self.lr = lr self.load = load if load is not None: self.save = self.load if save is None else save else: self.save = save self.data_path = data_path self.vocab_file = vocab_file DISTRUBUTED_CONFIG['nnodes'] = nnodes DISTRUBUTED_CONFIG['node_rank'] = node_rank DISTRUBUTED_CONFIG['nproc_per_node'] = nproc_per_node DISTRUBUTED_CONFIG['master_addr'] = master_addr DISTRUBUTED_CONFIG['master_port'] = master_port @staticmethod def _dict_to_cmd(config_dict): cmd = [] for k, v in config_dict.items(): if k == 'model': pass else: if v is True: cmd.append('--' + k.replace('_','-')) elif v is False: continue else: cmd.append('--{}={}'.format(k.replace('_','-'), v)) return cmd def _get_training_script_args(self, oneCardInference=False): global MODEL_CONFIG, DEFAULT_CONFIG if oneCardInference: tmp_config = MODEL_CONFIG[self.model] tmp_config['model_parallel_size'] = 1 assert self.model_parallel_size == 1, "> mp=1 when Your using OneCardInference!" else: tmp_config = MODEL_CONFIG[self.model] if self.model_parallel_size == 1: self.model_parallel_size = tmp_config['model_parallel_size'] default_config = copy.deepcopy({**tmp_config, **DEFAULT_CONFIG}) _vars = vars(self) default_config['load'] = self.load default_config['save'] = self.save default_config['data_path'] = self.data_path default_config['vocab_file'] = self.vocab_file default_config['lr_decay_iters'] = self.lr_decay_iters # _vars.pop('checkpoint_path') for k, v in _vars.items(): default_config[k] = v cmd = self._dict_to_cmd(default_config) return cmd pcl_pangu/pcl_pangu/model/evolution/evolution.py000066400000000000000000000133171443434453700225400ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2022/7/20 # @Author: 2022 PCL import os import sys from loguru import logger from pcl_pangu.context import check_context from pcl_pangu.model.launcher_torch import launch from pcl_pangu.model.evolution.config_evolution import DISTRUBUTED_CONFIG, model_config_gpu def train(config): print('----------------------------- training config -----------------------------') print("> Base Model: [evolution]") print("> Model Size: [{}]".format(config.model)) print("> data_path: {}".format(config.data_path)) print("> global batch_size: {}".format(config.batch_size)) print("> save to path: {}".format(config.save)) print('------------------------------ end of config -----------------------------') if check_context()=='pytorch': script_args = config._get_training_script_args() py_script = '/panguAlpha_pytorch/pretrain_evolution.py' run_pt(script_args, py_script) else: print("ERROR: wrong backend.") return 1 def fine_tune(config): print('--------------------------- finetune config -----------------------------') print("> Base Model: [evolution]") print("> Model Size: [{}]".format(config.model)) print("> data_path: {}".format(config.data_path)) print("> global batch_size: {}".format(config.batch_size)) print("> save to path: {}".format(config.save)) print('---------------------------- end of config -------------------------------') if check_context()=='pytorch': script_args = config._get_training_script_args() py_script = '/panguAlpha_pytorch/pretrain_evolution.py' run_pt(script_args, py_script) else: print("ERROR: wrong backend.") return 1 def inference(config,top_k=1,top_p=0.9,input=None,input_file=None,output_file=None, generate_max_tokens=128,oneCardInference=True): backend_context = check_context() assert generate_max_tokens > 0 and generate_max_tokens<=800, "> generate_max_tokens always in (0, 800]" print('--------------------------- inference config --------------------------') print("> Base Model: [evolution]") print("> Model Size: [{}]".format(config.model)) print("> global batch_size: {}".format(config.batch_size)) print("> generate_max_tokens length: {}".format(generate_max_tokens)) print('---------------------------- end of config ----------------------------') if backend_context=='pytorch': from .config_evolution import DEFAULT_CONFIG DEFAULT_CONFIG['finetune'] = True config.batch_size = 1 script_args = config._get_training_script_args(oneCardInference=oneCardInference) py_script = '/panguAlpha_pytorch/tools/generate_samples_epangu.py' script_args.append('--top-k={}'.format(top_k)) script_args.append('--top-p={}'.format(top_p)) if input is not None: script_args.append('--sample-input={}'.format(input.encode('utf-8').hex())) if input_file is not None: script_args.append('--sample-input-file={}'.format(input_file)) if output_file is not None: script_args.append('--sample-output-file={}'.format(output_file)) if generate_max_tokens is not None: script_args.append('--generate_max_tokens={}'.format(generate_max_tokens)) script_args.append('--isEvolution=True') run_pt(script_args, py_script) elif 'onnx-' in backend_context: from pcl_pangu.onnx_inference.infer import onnx_generate from pcl_pangu.tokenizer.tokenization_jieba import get_tokenizer num_threads = 2 past_path = None model_path = None for filename in os.listdir(config.load): if filename.endswith('.npy'): past_path = os.path.join(config.load, filename) if filename.endswith('.onnx'): model_path = os.path.join(config.load, filename) tokenizer = get_tokenizer() if input is not None: raw_text = input output_samples = onnx_generate(raw_text,model_path,tokenizer,past_path, topk=top_k,top_p=top_p,threads=num_threads, max_len=generate_max_tokens, backend=backend_context) print('Input is:', raw_text) print('Output is:', output_samples[len(raw_text):], flush=True) print() if input_file is not None: raw_texts = open(input_file, 'r').read().split('\n\n') write_output = print if output_file is not None: output_file = open(output_file, 'w') write_output = lambda x: output_file.write(x + '\n') for raw_text in raw_texts: output_samples = onnx_generate(raw_text,model_path,tokenizer,past_path, topk=top_k,top_p=top_p,threads=num_threads, max_len=generate_max_tokens, backend=backend_context) write_output('Input is: ' + raw_text) write_output('Output is: ' + output_samples[len(raw_text):]) write_output() else: print("ERROR: wrong backend.") return 1 def run_pt(script_args, py_script, **kwargs): current_dir = os.path.dirname(os.path.dirname(__file__)) sys.path.append(current_dir + '/panguAlpha_pytorch') py_script = current_dir + py_script logger.info("> Running {} with args: {}".format(py_script, script_args)) launch(training_script=py_script, training_script_args=script_args, **DISTRUBUTED_CONFIG, **kwargs) return 0 if __name__ == '__main__': config = model_config_gpu() inference(config) pcl_pangu/pcl_pangu/model/launcher_torch.py000066400000000000000000000052011443434453700214610ustar00rootroot00000000000000import sys import subprocess import os from loguru import logger # logger = logging.getLogger(__name__) def launch(nnodes=1, node_rank=0, nproc_per_node=1, master_addr="", master_port=29500, use_env=False, module=False, no_python=False, training_script_args=[],training_script='', **kwargs): # world size in terms of number of processes dist_world_size = nproc_per_node * nnodes # set PyTorch distributed related environmental variables current_env = os.environ.copy() current_env["MASTER_ADDR"] = master_addr current_env["MASTER_PORT"] = str(master_port) current_env["WORLD_SIZE"] = str(dist_world_size) processes = [] if 'OMP_NUM_THREADS' not in os.environ and nproc_per_node > 1: current_env["OMP_NUM_THREADS"] = str(1) print("*****************************************\n" "Setting OMP_NUM_THREADS environment variable for each process " "to be {} in default, to avoid your system being overloaded, " "please further tune the variable for optimal performance in " "your application as needed. \n" "*****************************************".format(current_env["OMP_NUM_THREADS"])) for local_rank in range(0, nproc_per_node): # each process's rank dist_rank = nproc_per_node * node_rank + local_rank current_env["RANK"] = str(dist_rank) current_env["LOCAL_RANK"] = str(local_rank) # spawn the processes with_python = not no_python cmd = [] if with_python: cmd = [sys.executable, "-u"] if module: cmd.append("-m") else: if not use_env: raise ValueError("When using the '--no_python' flag, you must also set the '--use_env' flag.") if module: raise ValueError("Don't use both the '--no_python' flag and the '--module' flag at the same time.") cmd.append(training_script) if not use_env: cmd.append("--local_rank={}".format(local_rank)) cmd.extend(training_script_args) # os.chdir(python_work_dir) # logger.info(os.getcwd()) # logger.info(os.path.abspath(__file__)) logger.debug("Spawning process {} with command: {}".format(dist_rank, cmd)) process = subprocess.Popen(cmd, env=current_env) processes.append(process) for process in processes: process.wait() if process.returncode != 0: raise subprocess.CalledProcessError(returncode=process.returncode, cmd=cmd) if __name__ == "__main__": launch() pcl_pangu/pcl_pangu/model/mPangu/000077500000000000000000000000001443434453700173405ustar00rootroot00000000000000pcl_pangu/pcl_pangu/model/mPangu/__init__.py000066400000000000000000000003041443434453700214460ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2022/7/20 # @Author: 2022 PCL from .mPangu import train, fine_tune, inference from .config_mPangu import model_config_gpu, model_config_npupcl_pangu/pcl_pangu/model/mPangu/__pycache__/000077500000000000000000000000001443434453700215505ustar00rootroot00000000000000pcl_pangu/pcl_pangu/model/mPangu/__pycache__/__init__.cpython-38.pyc000066400000000000000000000005131443434453700257360ustar00rootroot00000000000000U b@s(ddlmZmZmZddlmZmZdS))train fine_tune inference)model_config_gpumodel_config_npuN)mPangurrrZ config_mPangurrrrPD:\并行平台\codes\AISyn_develop\pcl_pangu\pcl_pangu\model\mPangu\__init__.pyspcl_pangu/pcl_pangu/model/mPangu/__pycache__/config_mPangu.cpython-38.pyc000066400000000000000000000100151443434453700267510ustar00rootroot00000000000000U Gb@sddlZddlmZmZddddddddd d ddd dd d ddddddaddddddZeZdddddddeddddddddddd d!d"d"d#d d$d%d&aGd'd(d(ZGd)d*d*Z dS)+N)vocab_4w vocab_13wi) num_layers hidden_sizenum_attention_heads seq_lengthmax_position_embeddingsmodel_parallel_sizei 'i()350M2B613B localhosti>s)nnodes node_ranknproc_per_node master_addr master_porti izgpt2-merges.txtmmapz949,50,1nccla2U0*#?cosineh㈵>g{Gz?g?Tdi FGPT2BPETokenizer)r batch_size train_iterslr_decay_iterssaveload data_path vocab_file merge_file data_implsplitdistributed_backendlrlr_decay_stylemin_lr weight_decay clip_gradwarmupcheckpoint_activations log_interval save_interval eval_interval eval_itersfp16finetunetokenizer_typec@sHeZdZddddddedddddd d fd d Zed dZdddZdS)model_config_gpurrri'rdataNrri|d|ddq |dkrJq q |d|dd|q |S)Nr@Tz--_-Fz--{}={})itemsappendreplaceformat) config_dictcmdkvrDrDrE _dict_to_cmdoszmodel_config_gpu._dict_to_cmdFcCs|rdt|jd<tt|jt}t|}|j|d<|j|d<|j|d<|j |d<|j |d<| D]\}}|||<qh| |}|S)Nrr r)r(r*r+r') MODEL_CONFIGr@copydeepcopyDEFAULT_CONFIGvarsr)r(r*r+r'rIrQ)rConeCardInferencedefault_config_varsrOrPrNrDrDrE_get_training_script_args~s       z*model_config_gpu._get_training_script_args)F)__name__ __module__ __qualname__rrF staticmethodrQrZrDrDrDrEr>Ls$ " r>c@s<eZdZddddddddedd d d d fd d Zdd dZd S)model_config_npurrrr!gư>ir?i@NFcCs||_||_||_||_||_||_||_| |_||_| dk rT| dkrL|jn| |_ n| |_ ||_ | |_ | |_ | |_ |jr|jdk stddS)Nz/> Please set your pretrained [model.ckpt] path!)r@r r%train_epoch_sizer'start_lrend_lrr)r<r(r*r+ vocab_sizestrategy_load_ckpt_pathAssertionError)rCr@r r%r`rarbr'r*r+rcr)r(rdr<rDrDrErFs$zmodel_config_npu.__init__cCs|rdt|jd<tt|jt}t|}|j|d<|j|d<|j|d<|j |d<|j |d<|j |d<|j |d <| D]\}}|||<q||S) Nrr r)r(r<r*r+rcr')rRr@rSrTrUrVr)r(r<r*r+rcr'rI)rCrWrXrYrOrPrDrDrE_cover_modelarts_training_argss        z/model_config_npu._cover_modelarts_training_args)F)r[r\r]rrFrfrDrDrDrEr_s  &r_) rSpcl_pangu.tokenizerrrrRrB vocab_dirrUr>r_rDrDrDrEsv  Dpcl_pangu/pcl_pangu/model/mPangu/__pycache__/mPangu.cpython-38.pyc000066400000000000000000000100541443434453700254270ustar00rootroot00000000000000U Gb@sddlZddlZddlmZddlmZddlmZddlm Z m Z m Z dd Z d d Z dddZddZddZddZedkre ZeedS)N)logger)launch) check_context)DISTRUBUTED_CONFIGmodel_config_gpumodel_config_npucCstdtdtd|jtd|jtd|jtd|jtdtdkrt|tspt | }d }t ||n(td krt|t st | }t|dS) NzF---------------------------- train config ----------------------------> Base Model: [alpha]> Model Size: [{}]> data_path: {}> global batch_size: {}> save to path: {}F--------------------------- end of config ----------------------------pytorch$/panguAlpha_pytorch/pretrain_gpt2.py mindspore)printformatmodel data_path batch_sizesaver isinstancerAssertionError_get_training_script_argsrun_ptr _cover_modelarts_training_args run_ms_train)config script_args py_script config_dictr#ND:\并行平台\codes\AISyn_develop\pcl_pangu\pcl_pangu\model\mPangu\mPangu.pytrains    r%cCstdtdtd|jtd|jtd|jtd|jtdtdkrd d lm}d |d <| }d }t ||n(tdkrt |t st |}t|dS)NzF-------------------------- finetune config ---------------------------r r r r rrrrDEFAULT_CONFIGTfinetunerr)rrrrrrr config_mPangur'rrrr rrr)rr'r r!r"r#r#r$ fine_tune$s"    r*?Tc Cs^tdtdtd|jtd|jtd|jtd|jtdtdkrd d lm}d |d <d |_|j |d }d} | d|| d||dk r| d| d |dk r| d||dk r| d|t || nLtdkrZt|ts*t|} || d<|| d<|| d<|| d<|| d<dS)NzG--------------------------- inference config --------------------------r r r r rzG---------------------------- end of config ----------------------------rrr&Tr()oneCardInferencez3/panguAlpha_pytorch/tools/generate_samples_Pangu.pyz --top-k={}z --top-p={}z--sample-input={}zutf-8z--sample-input-file={}z--sample-output-file={}rtop_ktop_pinput input_file output_file)rrrrrrrr)r'rappendencodehexrrr rr) rr-r.r/r0r1r,r'r r!r"r#r#r$ inference9s<      r5cKsXtjtjt}tj|d||}td||t f||dt |dS)Nz/panguAlpha_pytorchz> Running {} with args: {})training_scripttraining_script_args) ospathdirname__file__sysr2rinforrr)r r!kwargs current_dirr#r#r$r[srcCs\tjtjtjttjj}tj|dddl m }m }m }|||}||dS)N/panguAlpha_mindsporer)opt setup_argsmain) r8r9abspathjoinr:r;pardirr<r2train_alpha_ms13rArBrC)r"r?rArBrCnew_optr#r#r$rfs " rcCsjtjtjtjttjj}tj|dddl m }m }m }m }|||}|||}||dS)Nr@r)rArBextend_setup_args_for_mPangurC)r8r9rDrEr:r;rFr<r2inference_alpha_ms13rArBrIrC)r"r?rArBrIrCrHr#r#r$run_ms_inferencems "  rK__main__)rr+NNNT)r8r<logururlauncher_torchrpcl_pangu.contextrr)rrr r%r*r5rrrK__name__rr#r#r#r$s    " pcl_pangu/pcl_pangu/model/mPangu/config_mPangu.py000066400000000000000000000155331443434453700224750ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2022/7/20 # @Author: 2022 PCL import copy from pcl_pangu.tokenizer import vocab_4w, vocab_13w MODEL_CONFIG = { '350M': { 'num_layers': 23, 'hidden_size': 1024, 'num_attention_heads': 16, 'seq_length': 1024, 'max_position_embeddings': 1024, 'model_parallel_size': 1, }, '2B6': { 'num_layers': 31, 'hidden_size': 2560, 'num_attention_heads': 32, 'seq_length': 1024, 'max_position_embeddings': 1024, 'model_parallel_size': 2, # 2B6 mp_size >= 2 , recommend for [2,4,8] }, '13B': { 'num_layers': 39, 'hidden_size': 5120, 'num_attention_heads': 40, 'seq_length': 1024, 'max_position_embeddings': 1024, 'model_parallel_size': 8, # 13B mp_size >= 8 , recommend for [8] } } DISTRUBUTED_CONFIG = { 'nnodes':1, 'node_rank':0, 'nproc_per_node':1, 'master_addr':"localhost", 'master_port':29502 } vocab_dir = vocab_13w DEFAULT_CONFIG = { 'model_parallel_size': 1, 'batch_size': 8, 'train_iters': 500000, 'lr_decay_iters': 320000, 'save': '', 'load': '', 'data_path': '', 'vocab_file': vocab_dir, 'merge_file': 'gpt2-merges.txt', 'data_impl': 'mmap', 'split': '949,50,1', 'distributed_backend': 'nccl', 'lr': 0.00015, 'lr_decay_style': 'cosine', 'min_lr': 1.0e-5, 'weight_decay': 1e-2, 'clip_grad': 1.0, 'warmup': 0.01, 'checkpoint_activations': True, 'log_interval': 100, 'save_interval': 1000, 'eval_interval': 1000, 'eval_iters': 10, 'fp16': True, 'finetune': False, 'tokenizer_type': 'GPT2BPETokenizer' } class model_config_gpu(): def __init__(self, model='350M', model_parallel_size=1, batch_size=8, train_iters=10000, lr=0.00015, data_path='data', vocab_file=vocab_13w, load=None, save=None, nnodes=1, node_rank=0, nproc_per_node=1, master_addr="localhost", master_port=29500): self.model = model self.model_parallel_size = model_parallel_size self.batch_size = batch_size self.train_iters = train_iters self.lr_decay_iters = int(0.64 * self.train_iters) self.lr = lr self.load = load if load is not None: self.save = self.load if save is None else save else: self.save = save self.data_path = data_path self.vocab_file = vocab_file DISTRUBUTED_CONFIG['nnodes'] = nnodes DISTRUBUTED_CONFIG['node_rank'] = node_rank DISTRUBUTED_CONFIG['nproc_per_node'] = nproc_per_node DISTRUBUTED_CONFIG['master_addr'] = master_addr DISTRUBUTED_CONFIG['master_port'] = master_port @staticmethod def _dict_to_cmd(config_dict): cmd = [] for k, v in config_dict.items(): if k == 'model': pass else: if v is True: cmd.append('--' + k.replace('_','-')) elif v is False: continue else: cmd.append('--{}={}'.format(k.replace('_', '-'), v)) return cmd def _get_training_script_args(self, oneCardInference=False): global MODEL_CONFIG, DEFAULT_CONFIG if oneCardInference: tmp_config = MODEL_CONFIG[self.model] tmp_config['model_parallel_size'] = 1 assert self.model_parallel_size == 1, "> mp=1 when Your using OneCardInference!" else: tmp_config = MODEL_CONFIG[self.model] if self.model_parallel_size == 1: self.model_parallel_size = tmp_config['model_parallel_size'] default_config = copy.deepcopy({**tmp_config, **DEFAULT_CONFIG}) _vars = vars(self) default_config['load'] = self.load default_config['save'] = self.save default_config['data_path'] = self.data_path default_config['vocab_file'] = self.vocab_file default_config['lr_decay_iters'] = self.lr_decay_iters # _vars.pop('checkpoint_path') for k, v in _vars.items(): default_config[k] = v cmd = self._dict_to_cmd(default_config) return cmd class model_config_npu(): def __init__(self, model='350M', model_parallel_size=1, batch_size=8, train_iters=50000, start_lr=1.0e-5, end_lr=1.0e-6, data_path='data', vocab_file=vocab_13w, vocab_size=128320, load=None, save=None, strategy_load_ckpt_path=None, finetune=False, ): self.model = model self.model_parallel_size = model_parallel_size self.batch_size = batch_size self.train_iters = train_iters self.lr_decay_iters = int(0.64 * self.train_iters) self.start_lr = start_lr self.end_lr = end_lr self.load = load self.finetune = finetune if load is not None: self.save = self.load if save is None else save else: self.save = save self.data_path = data_path self.vocab_file = vocab_file self.vocab_size = vocab_size self.strategy_load_ckpt_path = strategy_load_ckpt_path if self.finetune: assert self.load is not None, "> Please set your pretrained [model.ckpt] path!" # assert self.strategy_load_ckpt_path is not None, "> Please set your pretrained model [strategy.ckpt] path!" def _cover_modelarts_training_args(self, oneCardInference=False): global MODEL_CONFIG, DEFAULT_CONFIG if oneCardInference: tmp_config = MODEL_CONFIG[self.model] tmp_config['model_parallel_size'] = 1 assert self.model_parallel_size == 1, "> mp=1 when Your using OneCardInference!" else: tmp_config = MODEL_CONFIG[self.model] if self.model_parallel_size == 1: self.model_parallel_size = tmp_config['model_parallel_size'] default_config = copy.deepcopy({**tmp_config, **DEFAULT_CONFIG}) _vars = vars(self) default_config['load'] = self.load default_config['save'] = self.save default_config['finetune'] = self.finetune default_config['data_path'] = self.data_path default_config['vocab_file'] = self.vocab_file default_config['vocab_size'] = self.vocab_size default_config['lr_decay_iters'] = self.lr_decay_iters # _vars.pop('checkpoint_path') for k, v in _vars.items(): default_config[k] = v return default_config pcl_pangu/pcl_pangu/model/mPangu/mPangu.py000066400000000000000000000226311443434453700211450ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2022/7/20 # @Author: 2022 PCL import os import sys from loguru import logger from ..launcher_torch import launch from pcl_pangu.context import check_context from .config_mPangu import DISTRUBUTED_CONFIG, model_config_gpu, model_config_npu def train(config): print('---------------------------- train config ----------------------------') print("> Base Model: [alpha]") print("> Model Size: [{}]".format(config.model)) print("> data_path: {}".format(config.data_path)) print("> global batch_size: {}".format(config.batch_size)) print("> save to path: {}".format(config.save)) print('--------------------------- end of config ----------------------------') if check_context()=='pytorch': assert isinstance(config, model_config_gpu) script_args = config._get_training_script_args() py_script = '/panguAlpha_pytorch/pretrain_gpt2.py' run_pt(script_args, py_script) elif check_context()=='mindspore': assert isinstance(config, model_config_npu) config_dict = config._cover_modelarts_training_args() run_ms_train(config_dict) def fine_tune(config): print('-------------------------- finetune config ---------------------------') print("> Base Model: [alpha]") print("> Model Size: [{}]".format(config.model)) print("> data_path: {}".format(config.data_path)) print("> global batch_size: {}".format(config.batch_size)) print("> save to path: {}".format(config.save)) print('--------------------------- end of config ----------------------------') if check_context()=='pytorch': from .config_mPangu import DEFAULT_CONFIG DEFAULT_CONFIG['finetune'] = True script_args = config._get_training_script_args() py_script = '/panguAlpha_pytorch/pretrain_gpt2.py' run_pt(script_args, py_script) elif check_context()=='mindspore': assert isinstance(config, model_config_npu) config_dict = config._cover_modelarts_training_args() run_ms_train(config_dict) run_ms_finetune_merge_OpenI(config_dict) def inference(config,top_k=1,top_p=0.9,input=None,input_file=None,output_file=None, src_language='zh', tag_language='kk', generate_max_tokens=128,oneCardInference=True): assert generate_max_tokens > 0 and generate_max_tokens <= 800, "> generate_max_tokens always in (0, 800]" print('--------------------------- inference config --------------------------') print("> Base Model: [alpha]") print("> Model Size: [{}]".format(config.model)) print("> global batch_size: {}".format(config.batch_size)) print("> generate_max_tokens length: {}".format(generate_max_tokens)) print('---------------------------- end of config ----------------------------') if check_context()=='pytorch': from .config_mPangu import DEFAULT_CONFIG DEFAULT_CONFIG['finetune'] = True config.batch_size = 1 script_args = config._get_training_script_args(oneCardInference=oneCardInference) py_script = '/panguAlpha_pytorch/tools/generate_samples_Pangu.py' script_args.append('--top-k={}'.format(top_k)) script_args.append('--top-p={}'.format(top_p)) if input is not None: script_args.append('--sample-input={}'.format(input.encode('utf-8').hex())) if input_file is not None: script_args.append('--sample-input-file={}'.format(input_file)) if output_file is not None: script_args.append('--sample-output-file={}'.format(output_file)) if generate_max_tokens is not None: script_args.append('--generate_max_tokens={}'.format(generate_max_tokens)) run_pt(script_args, py_script) elif check_context()=='mindspore': assert isinstance(config, model_config_npu) config_dict = config._cover_modelarts_training_args(oneCardInference=oneCardInference) config_dict['top_k'] = top_k config_dict['top_p'] = top_p config_dict['input'] = input config_dict['input_file'] = input_file config_dict['output_file'] = output_file config_dict['src_language'] = src_language config_dict['tag_language'] = tag_language config_dict['generate_max_tokens'] = generate_max_tokens run_ms_inference(config_dict) def run_pt(script_args, py_script, **kwargs): current_dir = os.path.dirname(os.path.dirname(__file__)) sys.path.append(current_dir+'/panguAlpha_pytorch') py_script = current_dir + py_script logger.info("> Running {} with args: {}".format(py_script, script_args)) launch(training_script=py_script, training_script_args=script_args, **DISTRUBUTED_CONFIG, **kwargs) def run_ms_train(config_dict): current_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)) sys.path.append(current_dir + '/panguAlpha_mindspore') from train_alpha_ms13 import opt, setup_args, main, mPangu_add_args new_opt = setup_args(opt, config_dict) new_opt = mPangu_add_args(new_opt) main(new_opt) def run_ms_inference(config_dict): current_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)) sys.path.append(current_dir + '/panguAlpha_mindspore') from inference_alpha_ms13 import opt, setup_args, extend_setup_args_for_mPangu, main new_opt = setup_args(opt, config_dict) new_opt = extend_setup_args_for_mPangu(new_opt, config_dict) main(new_opt) def run_ms_finetune_merge_OpenI(config_dict): current_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)) sys.path.append(current_dir + '/panguAlpha_mindspore') from train_alpha_ms13 import opt, setup_args, main, alpha_add_args, find_local_ckpt_maxStepNumber import mindspore as ms import mindspore.communication.management as D import moxing as mox import time D.init() device_num = D.get_group_size() rank_id = D.get_rank() args_opt = setup_args(opt, config_dict) args_opt = alpha_add_args(args_opt) # do merge sample_save_dir_rank = os.path.join(args_opt.save_checkpoint_path, f"rank_0") save_dir_strategy = os.path.join(args_opt.save_checkpoint_path, "strategy") strategy = ms.build_searched_strategy("/cache/strategy.ckpt") mp = args_opt.op_level_model_parallel_num # load mp slice modelWeight if rank_id % 8 == 0: maxStepNumber = find_local_ckpt_maxStepNumber(sample_save_dir_rank) sliced_parameters = [] for i in range(mp): save_dir_rank = os.path.join(args_opt.save_checkpoint_path, f"rank_{i}") print(save_dir_rank) os.system("ls {}".format(save_dir_rank)) save_ckptfile_name = args_opt.ckpt_name_prefix + '_' + str(i) model_prefix = os.path.join(save_dir_rank, "{}-{}_2.ckpt".format(save_ckptfile_name, maxStepNumber)) print(model_prefix) param_dict = ms.load_checkpoint(model_prefix) adam_names = [item for item in param_dict.keys() if 'adam' in item] for item in adam_names: param_dict.pop(item) param_dict.pop("scale_sense") param_dict.pop("global_step") param_dict.pop("current_iterator_step") param_dict.pop("last_overflow_iterator_step") param_dict.pop("epoch_num") param_dict.pop("step_num") sliced_parameters.append(param_dict) merged_parameter = {} for key in sliced_parameters[0].keys(): this_merged_list = [sliced_parameters[0].get(key), sliced_parameters[1].get(key)] this_merged_parameter = ms.merge_sliced_parameter(this_merged_list, strategy) merged_parameter[key] = this_merged_parameter # ops_cast = ops.Cast() param_list_fp16 = [] for (key, value) in merged_parameter.items(): if key == 'embedding_table': key = 'backbone.word_embedding.embedding_table' elif key == 'backbone.embedding.embedding.position_embedding.embedding_table': key = 'backbone.position_embedding.embedding_table' elif key == 'backbone.top_query_embedding_table': key = 'backbone.top_query_embedding.embedding_table' each_param_fp16 = {} each_param_fp16["name"] = key if isinstance(value.data, ms.Tensor): param_data_fp16 = ms.Tensor(value.data, ms.float16) # param_data_fp16 = ops_cast(value.data, ms.float16) else: param_data_fp16 = ms.Tensor(value.data, ms.float16) # param_data_fp16 = ops_cast(ms.Tensor(value.data), ms.float16) each_param_fp16["data"] = param_data_fp16 param_list_fp16.append(each_param_fp16) ms.save_checkpoint(param_list_fp16, "/{}/alpha-2B6-merged-{}_2-fp16.ckpt".format(args_opt.save_checkpoint_path, maxStepNumber)) try: mox.file.copy("/{}/alpha-2B6-merged-{}_2-fp16.ckpt".format(args_opt.save_checkpoint_path, maxStepNumber), os.path.join(args_opt.train_url, "alpha-2B6-merged-fp16.ckpt")) except: pass f = open("merge.txt", 'w') f.close() while not os.path.exists("merge.txt"): time.sleep(1) print('>>> merge mp={} model-slices to: {} >>>\n'.format(args_opt.op_level_model_parallel_num, args_opt.train_url)) exit() if __name__ == '__main__': config = model_config_gpu() run_pt(config) pcl_pangu/pcl_pangu/model/panguAlpha_mindspore/000077500000000000000000000000001443434453700222515ustar00rootroot00000000000000pcl_pangu/pcl_pangu/model/panguAlpha_mindspore/__init__.py000066400000000000000000000000001443434453700243500ustar00rootroot00000000000000pcl_pangu/pcl_pangu/model/panguAlpha_mindspore/inference_alpha_ms13.py000066400000000000000000000352221443434453700265750ustar00rootroot00000000000000# Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """ PanGu predict run """ import os import numpy as np import mindspore.common.dtype as mstype import mindspore.communication.management as D from mindspore import context, Tensor from mindspore import export from mindspore.context import ParallelMode from mindspore.parallel import set_algo_parameters from mindspore.parallel._cost_model_context import _set_multi_subgraphs from mindspore.train.model import Model from mindspore.train.serialization import load_checkpoint, load_param_into_net, load_distributed_checkpoint # from src.serialization import load_distributed_checkpoint from src.pangu_alpha import PanguAlpha, EvalNet from src.pangu_alpha_config import PANGUALPHAConfig, set_parse from src.utils_pangu import get_args import time from src.utils_pangu import get_openi_tar, ckpt_tar_openi, get_ckpt_file_list from mindspore.nn.wrap.cell_wrapper import PipelineCell, _VirtualDatasetCell def load_model(args_opt): r""" The main function for load model """ # Set execution mode context.set_context(save_graphs=False, mode=context.GRAPH_MODE, device_target=args_opt.device_target) context.set_context(variable_memory_max_size="30GB") print(args_opt) local_strategy_ckpt_path = args_opt.strategy_load_ckpt_path # Set parallel context if args_opt.distribute == "true": D.init() device_num = D.get_group_size() rank = D.get_rank() print("rank_id is {}, device_num is {}".format(rank, device_num)) if rank % 8 == 0: print(os.listdir(args_opt.load_ckpt_local_path), flush=True) context.reset_auto_parallel_context() if not local_strategy_ckpt_path is None: context.set_auto_parallel_context( parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL, gradients_mean=False, full_batch=True, loss_repeated_mean=True, enable_parallel_optimizer=False, strategy_ckpt_load_file=local_strategy_ckpt_path, # pipeline_stages=args_opt.stage_num ) else: context.set_auto_parallel_context( parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL, gradients_mean=False, full_batch=True, loss_repeated_mean=True, enable_parallel_optimizer=False ) set_algo_parameters(elementwise_op_strategy_follow=True) _set_multi_subgraphs() else: rank = 0 device_num = 1 context.reset_auto_parallel_context() if not local_strategy_ckpt_path is None: context.set_auto_parallel_context( strategy_ckpt_load_file=local_strategy_ckpt_path) else: context.set_auto_parallel_context() use_past = (args_opt.use_past == "true") print('local_rank:{}, start to run...'.format(rank), flush=True) if args_opt.export: use_past = True # Set model property model_parallel_num = args_opt.op_level_model_parallel_num data_parallel_num = int(device_num / model_parallel_num) # per_batch_size = args_opt.per_batch_size # batch_size = per_batch_size * data_parallel_num # Now only support single batch_size for predict batch_size = 1 if len(os.listdir(args_opt.load_ckpt_local_path)) <= 1: local_npy_path = None else: local_npy_path = args_opt.load_ckpt_local_path config = PANGUALPHAConfig( data_parallel_num=data_parallel_num, model_parallel_num=model_parallel_num, batch_size=batch_size, seq_length=args_opt.seq_length, vocab_size=args_opt.vocab_size, embedding_size=args_opt.embedding_size, num_layers=args_opt.num_layers, num_heads=args_opt.num_heads, expand_ratio=4, post_layernorm_residual=False, dropout_rate=0.0, compute_dtype=mstype.float16, use_past=use_past, stage_num=args_opt.stage_num, micro_size=args_opt.micro_size, eod_reset=False, word_emb_dp=True, load_ckpt_path=local_npy_path, param_init_type=mstype.float32 if args_opt.param_init_type == 'fp32' else mstype.float16) print("===config is: ", config, flush=True) print("=====args_opt is: ", args_opt, flush=True) ckpt_name = args_opt.load_ckpt_name # Define network pangu_alpha = PanguAlpha(config) ################################ eval_net = EvalNet(pangu_alpha) eval_net = _VirtualDatasetCell(eval_net) eval_net.set_train(False) ################################ model_predict = Model(eval_net) # Compile network and obtain tensor layout for loading ckpt inputs_np = Tensor(np.ones(shape=(config.batch_size, config.seq_length)), mstype.int32) current_index = Tensor(np.array([0]), mstype.int32) if local_strategy_ckpt_path is None: predict_layout = None elif config.use_past: batch_valid_length = Tensor(np.array([0]), mstype.int32) init_true = Tensor([True], mstype.bool_) inputs_np_1 = Tensor(np.ones(shape=(config.batch_size, 1)), mstype.int32) model_predict.predict_network.add_flags_recursive(is_first_iteration=True) predict_layout = model_predict.infer_predict_layout(inputs_np, current_index, init_true, batch_valid_length) model_predict.predict_network.add_flags_recursive(is_first_iteration=False) _ = model_predict.infer_predict_layout(inputs_np_1, current_index, init_true, batch_valid_length) else: predict_layout = model_predict.infer_predict_layout(inputs_np, current_index) OneCKPTPath = get_openi_tar(args_opt.load_ckpt_local_path) if rank % 8 == 0: ckpt_tar_openi(args_opt.load_ckpt_local_path) print("setting env success.") # 下载模型文件结束后，写一个文件来表示下载成功 f = open("/cache/get_ckpt.txt", 'w') f.close() while not os.path.exists("/cache/get_ckpt.txt"): time.sleep(1) ##------------------------------------------------------------------------------------------------------ print("======start load_distributed checkpoint", flush=True) if OneCKPTPath is None: if args_opt.model == '2B6': ckpt_file_list = get_ckpt_file_list(args_opt.load_ckpt_local_path, device_num=512) elif args_opt.model == '13B': ckpt_file_list = get_ckpt_file_list(args_opt.load_ckpt_local_path, device_num=512) else: raise ImportError('> Only Support 2B6 / 13B inference') print(f"Loading from path {ckpt_file_list[0]}", flush=True) load_distributed_checkpoint(eval_net, ckpt_file_list, predict_strategy=predict_layout) print("================load param ok=================", flush=True) else: print("Start to load one-ckpt checkpoint", flush=True) print(f"Loading from path {OneCKPTPath}", flush=True) parameters = load_checkpoint(OneCKPTPath) load_param_into_net(eval_net, parameters) print("================load param ok=================", flush=True) ##------------------------------------------------------------------------------------------------------- return model_predict, config def export_mindir(model_predict, config): """Export mindir model""" inputs_np = Tensor(np.ones(shape=(config.batch_size, config.seq_length)), mstype.int32) current_index = Tensor(np.array([0]), mstype.int32) batch_valid_length = Tensor(np.array([0]), mstype.int32) init_true = Tensor([True], mstype.bool_) inputs_np_1 = Tensor(np.ones(shape=(config.batch_size, 1)), mstype.int32) model_predict.predict_network.add_flags_recursive(is_first_iteration=True) export(model_predict.predict_network, inputs_np, current_index, init_true, batch_valid_length, file_name='pangu_alpha_1024', file_format='MINDIR') model_predict.predict_network.add_flags_recursive(is_first_iteration=False) export(model_predict.predict_network, inputs_np_1, current_index, init_true, batch_valid_length, file_name='pangu_alpha_1', file_format='MINDIR') print("Export finished and now exit.") def run_predict(model_predict, config, args_opt): """run predict""" from src.tokenization_jieba import JIEBATokenizer from src.generate import generate, generate_increment # Define tokenizer args_opt.end_token = 9 tokenizer = JIEBATokenizer(args_opt.tokenizer_path + '.vocab', args_opt.tokenizer_path + '.model') samples = args_opt.input input_file = args_opt.input_file output_file = args_opt.output_file if isinstance(samples, str): samples = [samples] elif isinstance(samples, list): pass else: raise ImportError("> only support string or list [input]!") if input_file is None: pass else: f = open(input_file, 'r', encoding='utf-8') all_data = f.readlines() f.close() samples = all_data.split('\n\n') if output_file is not None: out_f = open(output_file, 'w+', encoding='utf-8') else: out_f = None # Tokenize input sentence to ids for input_sentence in samples: print('> Input is:\n', input_sentence, flush=True) tokenized_token = tokenizer.tokenize(input_sentence) start_sentence = tokenizer.convert_tokens_to_ids(tokenized_token) input_ids = np.array(start_sentence).reshape(1, -1) # Call inference generate_func = generate_increment if config.use_past else generate output_ids = generate_func(model_predict, input_ids, args_opt) # Decode output ids to sentence output_samples = tokenizer.convert_ids_to_tokens(output_ids.tolist()) print('> Output is:\n', output_samples, flush=True) if not out_f is None: out_f.write('> Output is:\n{}'.format(output_samples)) if not out_f is None: out_f.close() def run_predict_mpg(model_predict, config, args_opt): """run predict""" # from src.tokenization_jieba import JIEBATokenizer from src.generate import generate, generate_increment from pcl_pangu.tokenizer.spm_13w.tokenizer import SpmTokenizer, langs_ID, translate_ID tokenizer = SpmTokenizer(args_opt.tokenizer_path) samples = args_opt.input input_file = args_opt.input_file output_file = args_opt.output_file if isinstance(samples, str): samples = [samples] elif isinstance(samples, list): pass else: raise ImportError("> only support string or list [input]!") if input_file is None: pass else: f = open(input_file, 'r', encoding='utf-8') all_data = f.readlines() f.close() samples = all_data.split('\n\n') if output_file is not None: out_f = open(output_file, 'w+', encoding='utf-8') else: out_f = None # Tokenize input sentence to ids for input_sentence in samples: print('> Input is:\n', input_sentence, flush=True) tokenized_token = tokenizer.tokenize(input_sentence) start_sentence = tokenizer.convert_tokens_to_ids(tokenized_token) start_sentence = [langs_ID[args_opt.src_language], langs_ID[args_opt.src_language], langs_ID[args_opt.src_language]] +\ start_sentence + \ [translate_ID, translate_ID, translate_ID] + \ [langs_ID[args_opt.tag_language], langs_ID[args_opt.tag_language], langs_ID[args_opt.tag_language]] input_ids = np.array(start_sentence).reshape(1, -1) # Call inference generate_func = generate_increment if config.use_past else generate output_ids = generate_func(model_predict, input_ids, args_opt) # Decode output ids to sentence output_samples = tokenizer.convert_ids_to_tokens(output_ids.tolist()) print('> Output is:\n', output_samples, flush=True) if not out_f is None: out_f.write('> Output is:\n{}'.format(output_samples)) if not out_f is None: out_f.close() def main(opt): """Main process for predict or export model""" model_predict, config = load_model(opt) if opt.export: export_mindir(model_predict, config) else: if opt.src_language == "": run_predict(model_predict, config, opt) else: run_predict_mpg(model_predict, config, opt) def setup_args(args_opt, model_config_dict): args_opt.op_level_model_parallel_num = model_config_dict.get('model_parallel_size') args_opt.per_batch_size = model_config_dict.get('batch_size') args_opt.model = model_config_dict.get('model') args_opt.seq_length = model_config_dict.get('seq_length') args_opt.embedding_size = model_config_dict.get('hidden_size') args_opt.num_layers = model_config_dict.get('num_layers') + 1 args_opt.num_heads = model_config_dict.get('num_attention_heads') args_opt.start_lr = model_config_dict.get('start_lr') args_opt.end_lr = model_config_dict.get('end_lr') args_opt.decay_steps = model_config_dict.get('lr_decay_iters') args_opt.epoch_size = model_config_dict.get('train_epoch_size') args_opt.strategy_load_ckpt_path = model_config_dict.get('strategy_load_ckpt_path') args_opt.save_checkpoint_path = model_config_dict.get('save') args_opt.load_ckpt_local_path = model_config_dict.get('load') args_opt.data_url = model_config_dict.get('data_path') args_opt.tokenizer_path = model_config_dict.get('vocab_file') args_opt.top_k = model_config_dict.get('top_k') args_opt.top_p = model_config_dict.get('top_p') args_opt.input = model_config_dict.get('input') args_opt.input_file = model_config_dict.get('input_file') args_opt.output_file = model_config_dict.get('output_file') args_opt.max_generate_length = model_config_dict.get('generate_max_tokens') return args_opt def extend_setup_args_for_mPangu(args_opt, model_config_dict): args_opt.vocab_size = model_config_dict.get('vocab_size') args_opt.src_language = model_config_dict.get('src_language') args_opt.tag_language = model_config_dict.get('tag_language') return args_opt opt = get_args(True)pcl_pangu/pcl_pangu/model/panguAlpha_mindspore/src/000077500000000000000000000000001443434453700230405ustar00rootroot00000000000000pcl_pangu/pcl_pangu/model/panguAlpha_mindspore/src/__init__.py000066400000000000000000000000001443434453700251370ustar00rootroot00000000000000pcl_pangu/pcl_pangu/model/panguAlpha_mindspore/src/dataset.py000066400000000000000000000215161443434453700250440ustar00rootroot00000000000000# Copyright 2022 PCL, Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """ Create dataset for training and evaluating """ import os import numpy as np import mindspore.dataset as ds import mindspore.dataset.transforms.c_transforms as C import mindspore.common.dtype as mstype def get_input_data_batch_slice_map(input_ids, eod_id, rank, dis, eod_reset): """ Generate position_id and attention_mask according to input_ids considering eod reset Inputs: input_ids: the input token ids eod_id: the id for rank: the current rank dis: the slice value for each rank eod_reset: whether to open eod reset or not returns: input_ids: the input token ids position_id: the position ids cosidering eod reset attention_mask: the attention mask considering eod reset """ rank = int(rank) input_ids = input_ids[rank * dis: (rank + 1) * dis] if not eod_reset: return input_ids seq_length = input_ids.shape[1] - 1 # Initialize position_ids and attention_mask batch_input_ids = input_ids batch_position_ids = np.ones((dis, seq_length)) batch_attention_mask = np.ones((dis, seq_length, seq_length)) # Loop through batches for bs_i, _ in enumerate(range(len(input_ids))): # Get normal position_ids and attention_mask local_ids = input_ids[bs_i] batch_attention_mask[bs_i] = np.tril(np.ones(shape=(seq_length, seq_length))) batch_position_ids[bs_i] = np.arange(seq_length) # Find eod_of_document eod_index = batch_position_ids[bs_i, local_ids[:-1] == eod_id].astype(np.int32) prev_index = 0 for i in range(eod_index.size): # Reset position_ids and attention_mask considering EOD index = eod_index[i] batch_attention_mask[bs_i, (index + 1):, :(index + 1)] = 0 batch_position_ids[bs_i, (index + 1):] -= (index + 1 - prev_index) prev_index = index + 1 return batch_input_ids, batch_position_ids, batch_attention_mask def create_dataset(batch_size, data_path, device_num=1, rank=0, drop=True, full_batch=False, data_start_index=0, eod_reset=False, eod_id=9, column_name='input_ids', epoch=1): """ Create dataset Inputs: batch_size: batch size data_path: path of your MindRecord files device_num: total device number rank: current rank id drop: whether drop remainder eod_reset: whether enable position reset and attention mask reset eod_id: the id for column_name: the column name of the mindrecord file. Default is input_ids epoch: The repeat times of the dataset Returns: dataset_restore: the dataset for training or evaluating """ ds.config.set_seed(1) # Control the size of data queue in the consideration of the memory ds.config.set_prefetch_size(1) # Get path for source data files home_path = os.path.join(os.getcwd(), data_path) files = os.listdir(data_path) data = [ os.path.join(home_path, name) for name in files if not name.endswith(".db") ] # Ensure the order of mindrecords is same in all machines, otherwise it will meet loss converge problem. data.sort() # print("000000000000000000", data) # Load data files and preprocess dataset = ds.MindDataset(data[data_start_index:], columns_list=[column_name], shuffle=False) type_cast_op = C.TypeCast(mstype.int32) type_cast_op_float = C.TypeCast(mstype.float16) if full_batch: # no need to slice from the inputs rank = 0 dis = batch_size else: # Each card slice a small batch from the full batch dis = int(batch_size / device_num) if batch_size % device_num != 0: raise ValueError( f"batch size {batch_size} should be a multiple of device number {device_num}." " You should change the args: per_batch_size.") map_func = (lambda input_ids: get_input_data_batch_slice_map(input_ids, eod_id, rank, dis, eod_reset)) # If eod_reset enabled, another two inputs will be generated through input_ids if eod_reset: dataset = dataset.batch(batch_size, drop_remainder=drop) dataset = dataset.map(operations=map_func, input_columns=[column_name], output_columns=[column_name, "position_id", "attention_mask"], column_order=[column_name, "position_id", "attention_mask"]) dataset = dataset.map(input_columns="position_id", operations=type_cast_op) dataset = dataset.map(input_columns="attention_mask", operations=type_cast_op_float) else: dataset = dataset.map(input_columns=[column_name], operations=type_cast_op) dataset = dataset.batch(batch_size, drop_remainder=drop) dataset = dataset.map(operations=map_func, input_columns=[column_name], output_columns=[column_name]) dataset = dataset.map(input_columns=column_name, operations=type_cast_op) dataset = dataset.repeat(epoch) return dataset def create_dataset_aisyn(batch_size, data_path, device_num=1, rank=0, drop=True, full_batch=False, data_start_index=0, eod_reset=False, eod_id=9, column_name='input_ids', epoch=1): """ Create dataset Inputs: batch_size: batch size data_path: path of your MindRecord files device_num: total device number rank: current rank id drop: whether drop remainder eod_reset: whether enable position reset and attention mask reset eod_id: the id for column_name: the column name of the mindrecord file. Default is input_ids epoch: The repeat times of the dataset Returns: dataset_restore: the dataset for training or evaluating """ # ds.config.set_seed(1) # Control the size of data queue in the consideration of the memory # ds.config.set_prefetch_size(1) # Get path for source data files home_path = os.path.join(os.getcwd(), data_path) files = os.listdir(data_path) data = [ os.path.join(home_path, name) for name in files if not name.endswith(".db") ] # Ensure the order of mindrecords is same in all machines, otherwise it will meet loss converge problem. data.sort() k_sample = 3 data_start_index = data_start_index%10 print("Using datasets:\n", len(data), data_start_index*k_sample, (data_start_index+1)*k_sample) # Load data files and preprocess dataset = ds.MindDataset(data[data_start_index*k_sample:(data_start_index+1)*k_sample], columns_list=[column_name], shuffle=False) type_cast_op = C.TypeCast(mstype.int32) type_cast_op_float = C.TypeCast(mstype.float16) if full_batch: # no need to slice from the inputs rank = 0 dis = batch_size else: # Each card slice a small batch from the full batch dis = int(batch_size / device_num) if batch_size % device_num != 0: raise ValueError( f"batch size {batch_size} should be a multiple of device number {device_num}." " You should change the args: per_batch_size.") map_func = (lambda input_ids: get_input_data_batch_slice_map(input_ids, eod_id, rank, dis, eod_reset)) # If eod_reset enabled, another two inputs will be generated through input_ids if eod_reset: dataset = dataset.batch(batch_size, drop_remainder=drop) dataset = dataset.map(operations=map_func, input_columns=[column_name], output_columns=[column_name, "position_id", "attention_mask"], column_order=[column_name, "position_id", "attention_mask"]) dataset = dataset.map(input_columns="position_id", operations=type_cast_op) dataset = dataset.map(input_columns="attention_mask", operations=type_cast_op_float) else: dataset = dataset.map(input_columns=[column_name], operations=type_cast_op) dataset = dataset.batch(batch_size, drop_remainder=drop) dataset = dataset.map(operations=map_func, input_columns=[column_name], output_columns=[column_name]) dataset = dataset.map(input_columns=column_name, operations=type_cast_op) dataset = dataset.repeat(epoch) return dataset pcl_pangu/pcl_pangu/model/panguAlpha_mindspore/src/generate.py000066400000000000000000000415221443434453700252100ustar00rootroot00000000000000# Copyright 2022 PCL, Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """ TopK for text generation """ import numpy as np import mindspore.common.dtype as mstype from mindspore.common.tensor import Tensor from mindspore.ops import operations as P def topk_fun(logits, topk=5): """Get topk""" target_column = logits[0].tolist() sorted_array = [(k, v) for k, v in enumerate(target_column)] sorted_array.sort(key=lambda x: x[1], reverse=True) topk_array = sorted_array[:topk] index, value = zip(*topk_array) index = np.array([index]) value = np.array([value]) return value, index def sampler(log_probs_revised, top_p, top_k, use_pynative=False): """Convert the log_probs to probability""" if use_pynative: logits = P.Pow()(10, Tensor(log_probs_revised, mstype.float32)) else: logits = np.power(10, np.array(log_probs_revised, np.float32)) # If top_p is less than 1.0, use top_p sampling if top_p < 1.0: # Only consider the 5000 largest logits to reduce computation if use_pynative: sorted_logits, index = P.TopK(sorted=True)(logits, 5000) cumsum_logits = P.CumSum()(sorted_logits, 1) cumsum_logits = cumsum_logits.asnumpy() index = index.asnumpy() sorted_logits = sorted_logits.asnumpy() else: sorted_logits, index = topk_fun(logits, 5000) cumsum_logits = np.cumsum(sorted_logits, 1) cumsum_logits = cumsum_logits[0] index = index[0] sorted_logits = sorted_logits[0] top_p_num = sum(cumsum_logits > top_p) # In case the probability is smooth, the sum of 5000 largest probabilities are not large enough if top_p_num == 0: top_p_num = 5000 # Get the corresponding probs and indices probs = sorted_logits[:top_p_num] p_args = index[:top_p_num] p = probs / sum(probs) # if top_p is set to 1.0, use top_k sampling else: # Get the corresponding probs and indices if use_pynative: probs, p_args = P.TopK(sorted=True)(logits, top_k) probs = probs.asnumpy() p_args = p_args.asnumpy() else: probs, p_args = topk_fun(logits, top_k) probs = probs[0] p_args = p_args[0] # Avoid rounding error if sum(probs) == 0: probs = np.array([1 / top_k for _ in range(top_k)]) p = probs / sum(probs) return p, p_args def generate(model, origin_inputs, config): """ Text generation Inputs: model: the model for inferencing origin_inputs: the original inputs based on which the model will continue writing config: inference configurations Returns: outputs: the ids for the generated text """ # Get configurations for inference frequency_penalty = config.frequency_penalty presence_penalty = config.presence_penalty top_p = config.top_p top_k = config.top_k max_generate_length = config.max_generate_length seq_length = config.seq_length end_token = config.end_token use_pynative = config.use_pynative_op _, valid_length = origin_inputs.shape # If target length exceeds seq_length, use seq_length instead target_length = valid_length + max_generate_length target_length = seq_length if target_length > seq_length else target_length # A list of the frequency of each token frequency_list = np.array([[0 for _ in range(config.vocab_size)]]) pad_length = seq_length - origin_inputs.shape[-1] # Pad original inputs to seq_length input_ids = np.pad(origin_inputs, ((0, 0), (0, pad_length)), 'constant', constant_values=(0, 0)) print("input_ids is ", input_ids) # A single loop generates one token, loop until reaching target seq_length or generating eod token while valid_length < target_length: inputs = Tensor(input_ids, mstype.int32) # Indicate the exact token position current_index = valid_length - 1 if valid_length - 1 > 0 else 0 current_index = Tensor([current_index], mstype.int32) # Call a single inference log_probs = model.predict(inputs, current_index) # Get the revised log_probs considering frequency and presence penalty to eliminate duplicate in generated results log_probs = log_probs.asnumpy().reshape(1, config.vocab_size) log_probs_revised = log_probs - frequency_list * frequency_penalty - (frequency_list > 0) * presence_penalty p, p_args = sampler(log_probs_revised, top_p, top_k, use_pynative) # Random select a token as final output for this round target_index = np.random.choice(len(p), p=p) # Stop judgment if p_args[target_index] == end_token or valid_length == target_length - 1: outputs = input_ids break # update frequency list target = p_args[target_index] frequency_list[0][target] = frequency_list[0][target] + 1 # Modify input_ids with newly generated token input_ids[0][valid_length] = p_args[target_index] valid_length += 1 # Return valid outputs out of padded outputs length = np.sum(outputs != 0) outputs = outputs[0][:length] return outputs def generate_increment(model, origin_inputs, config): """ Text generation for incremental inference Inputs: model: the model for inferencing origin_inputs: the original inputs based on which the model will continue writing config: inference configurations Returns: outputs: the ids for the generated text """ # Get configurations for inference frequency_penalty = config.frequency_penalty presence_penalty = config.presence_penalty top_p = config.top_p top_k = config.top_k max_generate_length = config.max_generate_length seq_length = config.seq_length end_token = config.end_token use_pynative = config.use_pynative_op _, valid_length = origin_inputs.shape # Init outputs with original inputs outputs = [origin_inputs[0][i] for i in range(valid_length)] # If target length exceeds seq_length, use seq_length instead target_length = valid_length + max_generate_length target_length = seq_length if target_length > seq_length else target_length # A list of the frequency of each token frequency_list = np.array([[0 for _ in range(config.vocab_size)]]) pad_length = seq_length - origin_inputs.shape[-1] # Pad original inputs to seq_length input_ids = np.pad(origin_inputs, ((0, 0), (0, pad_length)), 'constant', constant_values=(0, 0)) # print("input_ids is ", input_ids) # Indicate the exact token position current_index = valid_length - 1 if valid_length - 1 > 0 else 0 batch_valid_length = Tensor(np.array([current_index]), mstype.int32) current_index = Tensor(np.array([current_index]), mstype.int32) # For first graph, not_init should be false init_true = Tensor([True], mstype.bool_) init_false = Tensor([False], mstype.bool_) init = init_false # Claim the first graph model.predict_network.add_flags_recursive(is_first_iteration=True) # Call a single inference with input size of (bs, seq_length) logits = model.predict(Tensor(input_ids, mstype.int32), current_index, init, batch_valid_length) # Claim the second graph and set not_init to true init = init_true model.predict_network.add_flags_recursive(is_first_iteration=False) # A single loop generates one token, loop until reaching target seq_length or generating eod token while valid_length < target_length: # Reshape the output logits logits = logits.asnumpy() log_probs = logits.reshape(1, config.vocab_size) # Get the revised log_probs considering frequency and presence penalty to eliminate duplicate in generated results log_probs_revised = log_probs - frequency_list * frequency_penalty - (frequency_list > 0) * presence_penalty p, p_args = sampler(log_probs_revised, top_p, top_k, use_pynative) # Random select a token as final output for this round target_index = np.random.choice(len(p), p=p) # Stop judgment if p_args[target_index] == end_token or valid_length == target_length - 1: break # Update frequency list target = p_args[target_index] frequency_list[0][target] = frequency_list[0][target] + 1 valid_length += 1 batch_valid_length = Tensor(np.array([valid_length - 1]), mstype.int32) current_index = Tensor(np.array([0]), mstype.int32) input_id = Tensor([[target]], mstype.int32) # Update outputs with current generated token outputs.append(int(target)) # Call a single inference with input size of (bs, 1) logits = model.predict(input_id, current_index, init, batch_valid_length) # Return valid outputs out of padded outputs return np.array(outputs) def generate_increment_re(model, origin_inputs, config, dynamic_generate_length): """ Text generation for incremental inference Inputs: model: the model for inferencing origin_inputs: the original inputs based on which the model will continue writing config: inference configurations Returns: outputs: the ids for the generated text """ # Get configurations for inference frequency_penalty = config.frequency_penalty presence_penalty = config.presence_penalty top_p = config.top_p top_k = config.top_k max_generate_length = dynamic_generate_length ## config.max_generate_length seq_length = config.seq_length end_token = config.end_token use_pynative = config.use_pynative_op _, valid_length = origin_inputs.shape # Init outputs with original inputs outputs = [origin_inputs[0][i] for i in range(valid_length)] # If target length exceeds seq_length, use seq_length instead target_length = valid_length + max_generate_length target_length = seq_length if target_length > seq_length else target_length # A list of the frequency of each token frequency_list = np.array([[0 for _ in range(config.vocab_size)]]) pad_length = seq_length - origin_inputs.shape[-1] # Pad original inputs to seq_length input_ids = np.pad(origin_inputs, ((0, 0), (0, pad_length)), 'constant', constant_values=(0, 0)) # print("input_ids is ", input_ids) # Indicate the exact token position current_index = valid_length - 1 if valid_length - 1 > 0 else 0 batch_valid_length = Tensor(np.array([current_index]), mstype.int32) current_index = Tensor(np.array([current_index]), mstype.int32) # For first graph, not_init should be false init_true = Tensor([True], mstype.bool_) init_false = Tensor([False], mstype.bool_) init = init_false # Claim the first graph model.predict_network.add_flags_recursive(is_first_iteration=True) # Call a single inference with input size of (bs, seq_length) logits = model.predict(Tensor(input_ids, mstype.int32), current_index, init, batch_valid_length) # Claim the second graph and set not_init to true init = init_true model.predict_network.add_flags_recursive(is_first_iteration=False) # A single loop generates one token, loop until reaching target seq_length or generating eod token while valid_length < target_length: # Reshape the output logits logits = logits.asnumpy() log_probs = logits.reshape(1, config.vocab_size) # Get the revised log_probs considering frequency and presence penalty to eliminate duplicate in generated results log_probs_revised = log_probs - frequency_list * frequency_penalty - (frequency_list > 0) * presence_penalty p, p_args = sampler(log_probs_revised, top_p, top_k, use_pynative) # Random select a token as final output for this round target_index = np.random.choice(len(p), p=p) # Stop judgment if p_args[target_index] == end_token or valid_length == target_length - 1: break # Update frequency list target = p_args[target_index] # frequency_list[0][target] = frequency_list[0][target] + 1 valid_length += 1 batch_valid_length = Tensor(np.array([valid_length - 1]), mstype.int32) current_index = Tensor(np.array([0]), mstype.int32) input_id = Tensor([[target]], mstype.int32) # Update outputs with current generated token outputs.append(int(target)) # Call a single inference with input size of (bs, 1) logits = model.predict(input_id, current_index, init, batch_valid_length) # Return valid outputs out of padded outputs return np.array(outputs) def generate_increment_logits(model, origin_inputs, config, dynamic_generate_length): """ Text generation for incremental inference Inputs: model: the model for inferencing origin_inputs: the original inputs based on which the model will continue writing config: inference configurations Returns: outputs: the ids for the generated text """ # Get configurations for inference frequency_penalty = config.frequency_penalty presence_penalty = config.presence_penalty top_p = config.top_p top_k = config.top_k max_generate_length = dynamic_generate_length ## config.max_generate_length seq_length = config.seq_length end_token = config.end_token use_pynative = config.use_pynative_op _, valid_length = origin_inputs.shape # Init outputs with original inputs outputs = [origin_inputs[0][i] for i in range(valid_length)] # If target length exceeds seq_length, use seq_length instead target_length = valid_length + max_generate_length target_length = seq_length if target_length > seq_length else target_length # A list of the frequency of each token frequency_list = np.array([[0 for _ in range(config.vocab_size)]]) pad_length = seq_length - origin_inputs.shape[-1] # Pad original inputs to seq_length input_ids = np.pad(origin_inputs, ((0, 0), (0, pad_length)), 'constant', constant_values=(0, 0)) # print("input_ids is ", input_ids) # Indicate the exact token position current_index = valid_length - 1 if valid_length - 1 > 0 else 0 batch_valid_length = Tensor(np.array([current_index]), mstype.int32) current_index = Tensor(np.array([current_index]), mstype.int32) # For first graph, not_init should be false init_true = Tensor([True], mstype.bool_) init_false = Tensor([False], mstype.bool_) init = init_false # Claim the first graph model.predict_network.add_flags_recursive(is_first_iteration=True) # Call a single inference with input size of (bs, seq_length) logits = model.predict(Tensor(input_ids, mstype.int32), current_index, init, batch_valid_length) # Claim the second graph and set not_init to true init = init_true model.predict_network.add_flags_recursive(is_first_iteration=False) # A single loop generates one token, loop until reaching target seq_length or generating eod token while valid_length < target_length: # Reshape the output logits logits = logits.asnumpy() log_probs = logits.reshape(1, config.vocab_size) # Get the revised log_probs considering frequency and presence penalty to eliminate duplicate in generated results log_probs_revised = log_probs - frequency_list * frequency_penalty - (frequency_list > 0) * presence_penalty p, p_args = sampler(log_probs_revised, top_p, top_k, use_pynative) # Random select a token as final output for this round target_index = np.random.choice(len(p), p=p) # Stop judgment if p_args[target_index] == end_token or valid_length == target_length - 1: break # Update frequency list target = p_args[target_index] frequency_list[0][target] = frequency_list[0][target] + 1 valid_length += 1 batch_valid_length = Tensor(np.array([valid_length - 1]), mstype.int32) current_index = Tensor(np.array([0]), mstype.int32) input_id = Tensor([[target]], mstype.int32) # Update outputs with current generated token outputs.append(int(target)) # Call a single inference with input size of (bs, 1) logits = model.predict(input_id, current_index, init, batch_valid_length) # Return valid outputs out of padded outputs return np.array(logits)pcl_pangu/pcl_pangu/model/panguAlpha_mindspore/src/pangu_alpha.py000066400000000000000000001742331443434453700257030ustar00rootroot00000000000000# Copyright 2022 PCL, Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """PanguAlpha model""" import math import os import numpy as np import mindspore.nn as nn from mindspore.common.tensor import Tensor from mindspore.common.parameter import Parameter import mindspore.common.dtype as mstype from mindspore.common.initializer import initializer, Normal, TruncatedNormal from mindspore.ops import operations as P from mindspore.ops import functional as F from mindspore import context from mindspore.common.seed import _get_graph_seed from mindspore._checkparam import Validator class Dropout(nn.Cell): r""" A Dropout Implements with P.DropoutGenMask and P.DropoutDoMask for parallel training. """ def __init__(self, keep_prob=0.5, dtype=mstype.float32): super(Dropout, self).__init__() if keep_prob <= 0 or keep_prob > 1: raise ValueError( "dropout probability should be a number in range (0, 1], but got {}".format( keep_prob)) Validator.check_subclass("dtype", dtype, mstype.number_type, self.cls_name) Validator.check_value_type('keep_prob', keep_prob, [float], self.cls_name) self.keep_prob = keep_prob self.is_ascend = context.get_context('device_target') in ["Ascend"] if self.is_ascend: seed0, seed1 = _get_graph_seed(0, "dropout") self.seed0 = seed0 self.seed1 = seed1 self.dtype = dtype self.get_shape = P.Shape() self.dropout_gen_mask = P.DropoutGenMask(Seed0=self.seed0, Seed1=self.seed1) self.dropout_do_mask = P.DropoutDoMask() self.cast = P.Cast() else: self.dropout = P.Dropout(keep_prob) def construct(self, x): r""" Input: a tensor Returns: a tensor """ if not self.training: return x if not self.is_ascend: out, _ = self.dropout(x) return out if self.keep_prob == 1: return x shape = self.get_shape(x) dtype = P.DType()(x) keep_prob = self.cast(self.keep_prob, dtype) output = self.dropout_gen_mask(shape, keep_prob) return self.dropout_do_mask(x, output, keep_prob) def extend_repr(self): return 'keep_prob={}, dtype={}'.format(self.keep_prob, self.dtype) def shard(self, strategy): if self.is_ascend: self.dropout_gen_mask.shard(strategy) self.dropout_do_mask.shard(strategy) else: self.dropout.shard(strategy) class LayerNorm(nn.Cell): r""" A self-defined layer norm operation using reduce sum and reduce mean """ def __init__(self, normalized_shape, dp=4, eps=1e-5, parallel_optimizer=False): super(LayerNorm, self).__init__() self.gamma = Parameter(initializer('ones', normalized_shape), name="gamma", parallel_optimizer=parallel_optimizer) self.beta = Parameter(initializer('zeros', normalized_shape), name="beta", parallel_optimizer=parallel_optimizer) self.mean = P.ReduceMean(keep_dims=True).shard(((dp, 1, 1),)) self.square = P.Square().shard(((dp, 1, 1),)) self.sqrt = P.Sqrt().shard(((dp, 1, 1),)) self.sub1 = P.Sub().shard(((dp, 1, 1), (dp, 1, 1))) self.add = P.TensorAdd().shard(((dp, 1, 1), ())) self.mul = P.Mul().shard(((dp, 1, 1), (1,))) self.add2 = P.TensorAdd().shard(((dp, 1, 1), (1,))) self.real_div = P.RealDiv().shard(((dp, 1, 1), (dp, 1, 1))) self.eps = eps def construct(self, x): mean = self.mean(x, -1) diff = self.sub1(x, mean) variance = self.mean(self.square(diff), -1) variance_eps = self.sqrt(self.add(variance, self.eps)) output = self.real_div(diff, variance_eps) output = self.add2(self.mul(output, self.gamma), self.beta) return output class Mapping(nn.Cell): """ A mapping function with a 3d input Args: input_size: the size of the last dimension of the input tensor output_size: the desired size of the last dimension of the output tensor dtype: the compute datatype scale: the scale factor for initialization Inputs: x: the 3d input Returns: output: Tensor, a 3d tensor after projection """ # 优化：matmul，dtype, mapping_output def __init__(self, config, input_size, output_size, scale=1.0): super(Mapping, self).__init__() self.output_size = output_size self.input_size = input_size self.weight = Parameter(initializer(Normal(sigma=0.02 * scale), [input_size, output_size], config.param_init_type), name="mapping_weight") self.bias = Parameter(initializer("zeros", [ output_size, ], config.param_init_type), name="mapping_bias", parallel_optimizer=False) self.dtype = config.compute_dtype self.cast = P.Cast() self.add = P.TensorAdd().shard(((config.dp, 1), (1,))) self.matmul = P.MatMul().shard( ((config.dp, config.mp), (config.mp, 1))) def construct(self, x): out_shape = P.Shape()(x)[:-1] + (self.output_size,) x = P.Reshape()(x, (-1, self.input_size)) weight = self.cast(self.weight, self.dtype) x = self.matmul(x, weight) x = self.add(x, self.cast(self.bias, self.dtype)) output = P.Reshape()(x, out_shape) return output class MappingOutput(nn.Cell): """ A mapping function with a 3d input Args: input_size: the size of the last dimension of the input tensor output_size: the desired size of the last dimension of the output tensor dtype: the compute datatype scale: the scale factor for initialization Inputs: x: the 3d input Returns: output: Tensor, a 3d tensor after projection """ def __init__(self, config, input_size, output_size, scale=1.0): super(MappingOutput, self).__init__() self.output_size = output_size self.input_size = input_size self.weight = Parameter(initializer(Normal(sigma=0.02 * scale), [input_size, output_size], config.param_init_type), name="mapping_weight") self.bias = Parameter(initializer("zeros", [ output_size, ], config.param_init_type), name="mapping_bias") self.dtype = config.compute_dtype self.cast = P.Cast() self.add = P.TensorAdd().shard(((config.dp, config.mp), (config.mp,))) self.matmul = P.MatMul().shard(((config.dp, 1), (1, config.mp))) def construct(self, x): out_shape = P.Shape()(x)[:-1] + (self.output_size,) x = P.Reshape()(x, (-1, self.input_size)) weight = self.cast(self.weight, self.dtype) x = self.matmul(x, weight) x = self.add(x, self.cast(self.bias, self.dtype)) output = P.Reshape()(x, out_shape) return output class FeedForwardLayer(nn.Cell): """ The output mapping module for each layer Args: config(PanguAlphaConfig): the config of network scale: scale factor for initialization Inputs: x: output of the self-attention module Returns: output: Tensor, the output of this layer after mapping """ def __init__(self, config, scale=1.0): super(FeedForwardLayer, self).__init__() input_size = config.embedding_size output_size = config.embedding_size * config.expand_ratio # Project to expand_ratio*embedding_size self.mapping = MappingOutput(config, input_size, output_size) # Project back to embedding_size self.projection = Mapping(config, output_size, input_size, scale) self.activation = nn.GELU() self.activation.gelu.shard(((config.dp, 1, config.mp),)) self.dropout = Dropout(1 - config.dropout_rate) self.dropout.shard(((config.dp, 1, 1),)) def construct(self, x): # [bs, seq_length, expand_ratio*embedding_size] hidden = self.activation(self.mapping(x)) output = self.projection(hidden) # [bs, seq_length, expand_ratio] output = self.dropout(output) return output class EmbeddingLookup(nn.Cell): """ The embedding lookup table for vocabulary Inputs: input_ids: the tokenized inputs with datatype int32 Returns: output: Tensor, the embedding vector for the input with shape (batch_size, seq_length, embedding_size) self.embedding_table: Tensor, the embedding table for the vocabulary """ def __init__(self): super(EmbeddingLookup, self).__init__() self.gather = P.GatherV2() def construct(self, input_ids, table): output = self.gather(table, input_ids, 0) return output class Attention(nn.Cell): """ Self-Attention module for each layer Args: config(PanguAlphaConfig): the config of network scale: scale factor for initialization layer_idx: current layer index """ def __init__(self, config, scale=1.0, layer_idx=None): super(Attention, self).__init__() # Output layer self.projection = Mapping(config, config.embedding_size, config.embedding_size, scale) self.transpose = P.Transpose().shard(((config.dp, 1, config.mp, 1),)) self.merger_head_transpose = P.Transpose().shard( ((config.dp, config.mp, 1, 1),)) self.reshape = P.Reshape() self.n_head = config.num_heads # embedding size per head self.size_per_head = config.embedding_size // self.n_head self.concat_k = P.Concat(axis=3) self.concat_v = P.Concat(axis=2) self.multiply_data = Tensor([ -10000.0, ], dtype=mstype.float32) self.batch_matmul = P.BatchMatMul().shard( ((config.dp, config.mp, 1, 1), (config.dp, config.mp, 1, 1))) self.scale = scale self.real_div = P.RealDiv().shard(((config.dp, config.mp, 1, 1), ())) self.sub = P.Sub().shard( ((1,), (config.dp, 1, 1, 1))) self.mul = P.Mul().shard( ((config.dp, 1, 1, 1), (1,))) self.add = P.TensorAdd().shard( ((config.dp, 1, 1, 1), (config.dp, config.mp, 1, 1))) # Normalize factor for attention, sqrt(dk) as widely used if self.scale: self.scale_factor = Tensor(math.sqrt(self.size_per_head)) if layer_idx is not None: self.coeff = math.sqrt(layer_idx * math.sqrt(self.size_per_head)) self.coeff = Tensor(self.coeff) self.use_past = config.use_past self.dropout = Dropout(1 - config.dropout_rate) self.dropout.shard(((config.dp, 1, 1),)) self.prob_dropout = Dropout(1 - config.dropout_rate) self.prob_dropout.shard(((config.dp, config.mp, 1, 1),)) self.softmax = nn.Softmax() self.softmax.softmax.shard(((config.dp, config.mp, 1),)) self.expand_dims = P.ExpandDims().shard(((config.dp, 1, 1),)) dense_shape = [config.embedding_size, config.embedding_size] bias_shape = [config.embedding_size] # Query self.dense1 = nn.Dense(config.embedding_size, config.embedding_size, weight_init=initializer(init='normal', shape=dense_shape, dtype=config.param_init_type), bias_init=initializer(init='zeros', shape=bias_shape, dtype=config.param_init_type)).to_float(config.compute_dtype) self.dense1.matmul.shard(((config.dp, 1), (config.mp, 1))) self.dense1.bias_add.shard(((config.dp, config.mp), (config.mp,))) # Key self.dense2 = nn.Dense(config.embedding_size, config.embedding_size, weight_init=initializer(init='normal', shape=dense_shape, dtype=config.param_init_type), bias_init=initializer(init='zeros', shape=bias_shape, dtype=config.param_init_type)).to_float(config.compute_dtype) self.dense2.matmul.shard(((config.dp, 1), (config.mp, 1))) self.dense2.bias_add.shard(((config.dp, config.mp), (config.mp,))) # Value self.dense3 = nn.Dense(config.embedding_size, config.embedding_size, weight_init=initializer(init='normal', shape=dense_shape, dtype=config.param_init_type), bias_init=initializer(init='zeros', shape=bias_shape, dtype=config.param_init_type)).to_float(config.compute_dtype) self.dense3.matmul.shard(((config.dp, 1), (config.mp, 1))) self.dense3.bias_add.shard(((config.dp, config.mp), (config.mp,))) self.is_first_iteration = True self.dtype = config.compute_dtype self.use_past = config.use_past if self.use_past: # operators used for state reuse seq_range = np.arange(config.seq_length).reshape(1, 1, -1) self.range = Tensor(np.tile(seq_range, (config.batch_size, 1, 1)), mstype.int32) self.seq_length = config.seq_length self.attention_mask = Tensor(np.tril(np.ones(shape=(self.seq_length, self.seq_length))), mstype.int32) self.slice = P.StridedSlice().shard(((1, 1, 1, 1),)) self.not_equal = P.NotEqual().shard(((1, 1, 1, 1), ())) self.reducesum = P.ReduceSum().shard(((1, 1, 1, 1),)) self.expand_dims = P.ExpandDims().shard(((1, 1, 1),)) self.tensor_le = P.LessEqual().shard(((1, 1, 1), (1, 1, 1))) self.add = P.TensorAdd().shard(((1, 1, 1, 1), (1, 1, 1, 1))) self.equal = P.Equal().shard(((1, 1, 1), (1, 1, 1))) self.sub1 = P.Sub().shard(((1,), ())) self.tile = P.Tile().shard(((1, 1, 1, 1),)) self.less = P.Less().shard(((1, 1, 1), (1, 1, 1))) self.mul1 = P.Mul().shard(((1, 1, 1, 1), (1, 1, 1, 1))) def construct(self, x, attention_mask, key_past=None, value_past=None, batch_valid_length=None): """ self-attention Inputs: x: output of previous layer attention_mask: the attention mask matrix with shape (batch_size, 1, seq_length, seq_length) key_past: previous saved key state value_past: previous saved value state batch_valid_length: the valid input seq_length without padding Returns: output: Tensor, the output logit of this layer layer_present: Tensor, the feature map of current layer """ original_shape = F.shape(x) x = F.reshape(x, (-1, original_shape[-1])) # Self attention: query, key, value are derived from the same inputs query = self.dense1(x) key = self.dense2(x) value = self.dense3(x) # [bs, num_heads, seq_length, size_per_head] query = self.transpose( F.reshape( query, (-1, original_shape[1], self.n_head, self.size_per_head)), (0, 2, 1, 3)) # [bs, num_heads, size_per_head, seq_length] key = self.transpose( F.reshape( key, (-1, original_shape[1], self.n_head, self.size_per_head)), (0, 2, 3, 1)) # [bs, num_heads, seq_length, size_per_head] value = self.transpose( F.reshape( value, (-1, original_shape[1], self.n_head, self.size_per_head)), (0, 2, 1, 3)) # key and value for current token(s) key_present = key value_present = value if self.use_past: # The first graph with the input size of (bs, seq_length) if self.is_first_iteration: # Get the valid input length without padding valid_length_vector = F.cast(self.less(self.range, batch_valid_length.view(1, 1, -1)), self.dtype) # Cover the key and value numbers corresponding to the padding position key_present = self.mul1(key, self.expand_dims(valid_length_vector, 2)) value_present = self.mul1(value, self.expand_dims(valid_length_vector, 3)) # The second graph with the inpus size of (bs, 1) # the shape of query is (bs, num_heads, 1, size_per_head) # the shape of key is (bs, num_heads, size_per_head, 1) # the shape of value is (bs, num_heads, 1, size_per_head) else: # Get the current token position index valid_length = self.reducesum(F.cast(self.not_equal(self.slice(key_past, (0, 0, 0, 0), (F.shape(x)[0], 1, 1, self.seq_length), (1, 1, 1, 1)), 0), mstype.float32), (1, 2, 3)) valid_length = F.reshape(valid_length, (-1, 1, 1)) valid_length_vector = F.cast(self.equal(valid_length, self.range), self.dtype) # Pad the key and value to seq_length with only the position index not zero current_key = self.mul1(self.tile(key, (1, 1, 1, self.seq_length)), self.expand_dims(valid_length_vector, 2)) current_value = self.mul1(self.tile(value, (1, 1, self.seq_length, 1)), self.expand_dims(valid_length_vector, 3)) # Concat the previous saved state and current state key = self.add(key_past, current_key) value = self.add(value_past, current_value) # Update key_present and value_present for state update key_present = key value_present = value attention_mask = F.reshape(self.attention_mask, (self.seq_length, self.seq_length, 1, 1)) layer_present = (key_present, value_present) # Self-attention considering attention mask attention = self._attn(query, key, value, attention_mask) # [bs, seq_length, embedding_size] attention_merge = self.merge_heads(attention) # Output output = self.projection(attention_merge) output = self.dropout(output) return output, layer_present def split_heads(self, x, transpose): """ split 3d tensor to 4d and switch certain axes Inputs: x: input tensor transpose: tuple, the transpose sequence Returns: x_transpose: the 4d output """ x_size = P.Shape()(x) new_x_shape = x_size[:-1] + (self.n_head, self.size_per_head) x = self.reshape(x, new_x_shape) x_transpose = self.transpose(x, transpose) return x_transpose def merge_heads(self, x): """ convert a 4d input to a 3d output Inputs: x: input tensor Returns: x_merge: the 3d output """ x = self.merger_head_transpose( x, (0, 2, 1, 3)) # bs, seq_length, head, size_per_head x_shape = P.Shape()(x) new_shape = x_shape[:-2] + (x_shape[-2] * x_shape[-1],) x_merge = self.reshape(x, new_shape) return x_merge def _attn(self, query, key, value, attention_mask): """ Get the weighted score along the seq_length Inputs: query: the query matrix key: the key matrix value: the value matrix attention_mask: the attention mask matrix with shape (batch_size, 1, seq_length, seq_length) Returns: weighted_values: Tensor, the weighted sum scores """ # Normalize query and key before MatMul, default off if not self.scale: query = query / F.cast(self.coeff, F.dtype(query)) key = key / F.cast(self.coeff, F.dtype(key)) # Attention score [bs, num_heads, seq_length_q, seq_length_k] score = self.batch_matmul(query, key) # Normalize after query and key MatMul, default on if self.scale: score = self.real_div( score, P.Cast()(self.scale_factor, P.DType()(score))) ori_dtype = P.DType()(score) score = P.Cast()(score, mstype.float32) # for input size of (bs, 1) namely the second graph, the shape of attention_mask matrix should be # (bs, 1, 1, seq_length) if self.use_past and not self.is_first_iteration: # Calculate the current total token current_index = self.reducesum(F.cast(self.not_equal(self.slice(key, (0, 0, 0, 0), (F.shape(query)[0], 1, 1, self.seq_length), (1, 1, 1, 1)), 0), mstype.float32), (1, 2, 3)) # Get the precise position index index = self.sub1(F.cast(current_index, mstype.int32), 1) index = F.reshape(index, (-1, 1, 1)) # Calculate the attention_mask matrix via the position index attention_mask = F.cast(self.tensor_le(self.range, index), mstype.int32) attention_mask = self.expand_dims(attention_mask, 2) # Minus 10000 for the position where masked to exclude them from softmax multiplu_out = self.sub( P.Cast()(F.tuple_to_array((1.0,)), P.DType()(score)), P.Cast()(attention_mask, P.DType()(score))) adder = self.mul(multiplu_out, self.multiply_data) attention_scores = self.add(adder, score) shape = F.shape(attention_scores) # attention probs attention_probs = self.softmax( F.reshape(attention_scores, (shape[0], -1, shape[-1]))) attention_probs = P.Cast()(attention_probs, ori_dtype) attention_probs = F.reshape(attention_probs, shape) attention_probs = self.prob_dropout(attention_probs) # Weighted sum output [bs, num_heads, seq_length_q, size_per_head] weighted_values = self.batch_matmul(attention_probs, value) return weighted_values class Decoder(nn.Cell): """ The basic decoder structure of PanguAlpha network Args: config(PanguAlphaConfig): the config of network layer_idx: current layer index Inputs: x: the output of previous layer(input_ids for the first layer) attention_mask: the attention mask matrix with shape (batch_size, 1, seq_length, seq_length) init_reset: whether reset the previous state batch_valid_length: the valid input seq_length without padding Returns: output: Tensor, the output logit of this layer layer_present: Tensor, the feature map of current layer """ def __init__(self, config, layer_idx): super(Decoder, self).__init__() scale = 1 / math.sqrt(2.0 * config.num_layers) self.layernorm1 = LayerNorm((config.embedding_size,), config.dp).to_float(mstype.float32) self.layernorm2 = LayerNorm((config.embedding_size,), config.dp).to_float(mstype.float32) self.attention = Attention(config, scale, layer_idx) # Feed Forward Network, FFN self.output = FeedForwardLayer(config, scale) self.post_layernorm_residual = config.post_layernorm_residual self.add = P.TensorAdd().shard(((config.dp, 1, 1), (config.dp, 1, 1))) # Last activation of this layer will be saved for recompute in backward process self.dtype = config.compute_dtype self.use_past = config.use_past if self.use_past: # operator used for state reuse self.reducesum = P.ReduceSum().shard(((1, 1, 1, 1),)) self.not_equal = P.NotEqual().shard(((1, 1, 1, 1), ())) self.slice = P.StridedSlice().shard(((1, 1, 1, 1),)) size_per_head = int(config.embedding_size / config.num_heads) self.key_shape = (config.batch_size, config.num_heads, size_per_head, config.seq_length) self.value_shape = (config.batch_size, config.num_heads, config.seq_length, size_per_head) # parameters saving key and value states self.key_past = Parameter(Tensor(np.zeros(shape=self.key_shape), self.dtype), name="key_past") self.value_past = Parameter(Tensor(np.zeros(shape=self.value_shape), self.dtype), name="value_past") self.tile = P.Tile().shard(((1, 1),)) self.mul = P.Mul().shard(((1, 1, 1, 1), (1,))) self.assign = P.Assign().shard(((1, 1, 1, 1), (1, 1, 1, 1))) def construct(self, x, input_mask, init_reset=True, batch_valid_length=None): r""" The forward process of the block. """ # [bs, seq_length, embedding_size] input_x = self.layernorm1(x) input_x = F.cast(input_x, self.dtype) # indicate whether reset saved states key_reset = None value_reset = None if self.use_past: # reset states, init_reset True for reuse and False for reset key_reset = self.assign(self.key_past, self.mul(self.key_past, F.cast(init_reset, self.dtype))) value_reset = self.assign(self.value_past, self.mul(self.value_past, F.cast(init_reset, self.dtype))) # add dependency for desired execution order input_x = F.depend(input_x, key_reset) input_x = F.depend(input_x, value_reset) attention, layer_present = self.attention(input_x, input_mask, self.key_past, self.value_past, batch_valid_length) # For post-layernorm the inputs for residual path are output of self-attention and output of layernorm if self.post_layernorm_residual: x = self.add(input_x, attention) # For pre-layernorm the inputs for residual path are output of self-attention and input of this layer else: x = self.add(x, attention) output_x = self.layernorm2(x) output_x = F.cast(output_x, self.dtype) mlp_logit = self.output(output_x) value_update = None key_update = None if self.use_past: # current key and value key_present, value_present = layer_present # update key and value calculated this step key_update = self.assign(self.key_past, key_present) value_update = self.assign(self.value_past, value_present) # add dependency for desired execution order key_update = F.depend(key_update, key_reset) value_update = F.depend(value_update, value_reset) # add dependency for desired execution order mlp_logit = F.depend(mlp_logit, value_update) mlp_logit = F.depend(mlp_logit, key_update) if self.post_layernorm_residual: output = self.add(output_x, mlp_logit) else: output = self.add(x, mlp_logit) return output, layer_present class Embedding(nn.Cell): """ Embedding """ def __init__(self, config): super(Embedding, self).__init__() self.word_embedding = EmbeddingLookup().set_comm_fusion(1) if config.word_emb_dp: self.word_embedding.gather.shard(((1, 1), (config.dp, 1))) else: self.word_embedding.gather.shard(((config.mp, 1), (1, 1))) if config.stage_num > 1: self.position_embedding = nn.Embedding(config.seq_length, config.embedding_size, embedding_table=Normal(0.02)).set_comm_fusion(1) else: # Position embedding if config.load_ckpt_path: # Loading the embedding table from the ckpt path: embedding_path = os.path.join(config.load_ckpt_path, 'position_embedding.npy') if os.path.exists(embedding_path): p_table = np.load(embedding_path) position_table_param = Tensor(p_table, mstype.float32) print("###### loading Position embedding Table successed! ######") else: raise ValueError(f"{embedding_path} file not exits, " f"please check whether position_embedding file exit.") else: position_table_param = TruncatedNormal(0.02) # Position embedding self.position_embedding = nn.Embedding( config.seq_length, config.embedding_size, embedding_table=position_table_param).set_comm_fusion(1) self.position_embedding.embedding_table.parallel_optimizer = False self.position_embedding.gather.shard(((1, 1), (config.dp,))) self.position_embedding.expand.shard(((config.dp, 1),)) self.add = P.TensorAdd().shard(((config.dp, 1, 1), (config.dp, 1, 1))) self.dropout = Dropout(1 - config.dropout_rate) self.dropout.shard(((config.dp, 1, 1),)) self.use_past = config.use_past self.is_first_iteration = True def construct(self, input_ids, table, input_position, valid_index=None): input_embedding = self.word_embedding(input_ids, table) if self.use_past and not self.is_first_iteration: _, seq_length = F.shape(input_ids) input_position = valid_index.view(1, seq_length) position_embedding = self.position_embedding(input_position) hidden_states = self.add(input_embedding, position_embedding) hidden_states = self.dropout(hidden_states) hidden_states = P.Cast()(hidden_states, mstype.float16) return hidden_states class Mask(nn.Cell): """ Mask """ def __init__(self, config): super(Mask, self).__init__() self.dtype = config.compute_dtype self.expand_dims = P.ExpandDims().shard(((config.dp, 1, 1),)) def construct(self, attention_mask): attention_mask = self.expand_dims(attention_mask, 1) return attention_mask class QueryLayerAttention(Attention): r""" Self-Attention module using input query vector. """ def construct(self, x, query_hidden_state, attention_mask, key_past=None, value_past=None, batch_valid_length=None): original_shape = F.shape(x) x = F.reshape(x, (-1, original_shape[-1])) query_hidden_state = F.reshape(query_hidden_state, (-1, original_shape[-1])) # For query_layer_attention, query are derived from outputs of previous layer and key, value are derived from an added parameter query_embedding query = self.dense1(query_hidden_state) key = self.dense2(x) value = self.dense3(x) query = self.transpose( F.reshape( query, (-1, original_shape[1], self.n_head, self.size_per_head)), (0, 2, 1, 3)) key = self.transpose( F.reshape( key, (-1, original_shape[1], self.n_head, self.size_per_head)), (0, 2, 3, 1)) value = self.transpose( F.reshape( value, (-1, original_shape[1], self.n_head, self.size_per_head)), (0, 2, 1, 3)) key_present = key value_present = value if self.use_past: # The first graph with the input size of (bs, seq_length) if self.is_first_iteration: # Get the valid input length without padding valid_length_vector = F.cast(self.less(self.range, batch_valid_length.view(1, 1, -1)), self.dtype) # Cover the key and value numbers corresponding to the padding position key_present = self.mul1(key, self.expand_dims(valid_length_vector, 2)) value_present = self.mul1(value, self.expand_dims(valid_length_vector, 3)) # The second graph with the inpus size of (bs, 1) # the shape of query is (bs, num_heads, 1, size_per_head) # the shape of key is (bs, num_heads, size_per_head, 1) # the shape of value is (bs, num_heads, 1, size_per_head) else: # Get the current token position index valid_length = self.reducesum(F.cast(self.not_equal(self.slice(key_past, (0, 0, 0, 0), (F.shape(x)[0], 1, 1, self.seq_length), (1, 1, 1, 1)), 0), mstype.float32), (1, 2, 3)) valid_length = F.reshape(valid_length, (-1, 1, 1)) valid_length_vector = F.cast(self.equal(valid_length, self.range), self.dtype) # Pad the key and value to seq_length with only the position index not zero current_key = self.mul1(self.tile(key, (1, 1, 1, self.seq_length)), self.expand_dims(valid_length_vector, 2)) current_value = self.mul1(self.tile(value, (1, 1, self.seq_length, 1)), self.expand_dims(valid_length_vector, 3)) # Concat the previous saved state and current state key = self.add(key_past, current_key) value = self.add(value_past, current_value) # Update key_present and value_present for state update key_present = key value_present = value attention_mask = F.reshape(self.attention_mask, (self.seq_length, self.seq_length, 1, 1)) layer_present = (key_present, value_present) attention = self._attn(query, key, value, attention_mask) attention_merge = self.merge_heads(attention) output = self.projection(attention_merge) output = self.dropout(output) return output, layer_present class QueryLayer(nn.Cell): r""" A block usingooked out position embedding as query vector. This is used as the final block. """ def __init__(self, config): super(QueryLayer, self).__init__() scale = 1 / math.sqrt(2.0 * config.num_layers) self.layernorm1 = LayerNorm((config.embedding_size,), config.dp).to_float(mstype.float32) self.layernorm2 = LayerNorm((config.embedding_size,), config.dp).to_float(mstype.float32) self.attention = QueryLayerAttention(config, scale) self.output = FeedForwardLayer(config, scale) self.post_layernorm_residual = config.post_layernorm_residual self.add = P.TensorAdd().shard(((config.dp, 1, 1), (config.dp, 1, 1))) self.dtype = config.compute_dtype self.use_past = config.use_past if self.use_past: # operator used for state reuse self.reducesum = P.ReduceSum().shard(((1, 1, 1, 1),)) self.not_equal = P.NotEqual().shard(((1, 1, 1, 1), ())) self.slice = P.StridedSlice().shard(((1, 1, 1, 1),)) size_per_head = int(config.embedding_size / config.num_heads) self.key_shape = (config.batch_size, config.num_heads, size_per_head, config.seq_length) self.value_shape = (config.batch_size, config.num_heads, config.seq_length, size_per_head) # parameters saving key and value states self.key_past = Parameter(Tensor(np.zeros(shape=self.key_shape), self.dtype), name="key_past") self.value_past = Parameter(Tensor(np.zeros(shape=self.value_shape), self.dtype), name="value_past") self.tile = P.Tile().shard(((1, 1),)) self.mul = P.Mul().shard(((1, 1, 1, 1), (1,))) self.assign = P.Assign().shard(((1, 1, 1, 1), (1, 1, 1, 1))) def construct(self, x, query_hidden_state, input_mask, init_reset=True, batch_valid_length=None): r""" Query Layer shares a similar structure with normal layer block except that it is not a traditional self-attention. """ input_x = self.layernorm1(x) input_x = F.cast(input_x, self.dtype) # indicate whether reset saved states key_reset = None value_reset = None if self.use_past: # reset states, init_reset True for reuse and False for reset key_reset = self.assign(self.key_past, self.mul(self.key_past, F.cast(init_reset, self.dtype))) value_reset = self.assign(self.value_past, self.mul(self.value_past, F.cast(init_reset, self.dtype))) # add dependency for desired execution order input_x = F.depend(input_x, key_reset) input_x = F.depend(input_x, value_reset) attention, layer_present = self.attention(input_x, query_hidden_state, input_mask, self.key_past, self.value_past, batch_valid_length) if self.post_layernorm_residual: x = self.add(input_x, attention) else: x = self.add(x, attention) output_x = self.layernorm2(x) output_x = F.cast(output_x, self.dtype) mlp_logit = self.output(output_x) value_update = None key_update = None if self.use_past: # current key and value key_present, value_present = layer_present # update key and value calculated this step key_update = self.assign(self.key_past, key_present) value_update = self.assign(self.value_past, value_present) # add dependency for desired execution order key_update = F.depend(key_update, key_reset) value_update = F.depend(value_update, value_reset) # add dependency for desired execution order mlp_logit = F.depend(mlp_logit, value_update) mlp_logit = F.depend(mlp_logit, key_update) if self.post_layernorm_residual: output = self.add(output_x, mlp_logit) else: output = self.add(x, mlp_logit) return output, layer_present class PanguAlphaEmbedding(nn.Cell): """ Input embedding, i.e., word embedding and position embedding Args: config(PanguAlphaConfig): the config of network Inputs: input_ids: the tokenized inputs with datatype int32 input_mask: the mask indicating whether each position is a valid input input_position: the position index of each token attention_mask: the attention_mask attention for self-attention module valid_index: only used in incremental inference, the position index of current token outputs: hidden_states: Tensor, input embeddings attention_mask: Tensor, attention_mask matrix embedding_table: Tensor, embedding_table with shape of (vocab_size, embedding_size) """ def __init__(self, config): super(PanguAlphaEmbedding, self).__init__() self.embedding = Embedding(config) if config.stage_num > 1: self.embedding.pipeline_stage = 0 self.mask = Mask(config) def construct(self, input_ids, input_mask, table, input_position, attention_mask, valid_index=None): """ Calculate input embeddings via input token ids and input position """ hidden_states = self.embedding(input_ids, table, input_position, valid_index) attention_mask = self.mask(attention_mask) return hidden_states, attention_mask class PanguAlpha_Model(nn.Cell): """ The backbone of PanguAlpha network Args: config(PanguAlphaConfig): the config of network Inputs: input_ids: the tokenized inputs with datatype int32 input_mask: the mask indicating whether each position is a valid input layer_past: the previous feature map Returns: output_state: Tensor, the output logit of backbone present_layer: Tensor, the current feature map embedding_table: Tensor, the embedding table for the vocabulary """ def __init__(self, config): super(PanguAlpha_Model, self).__init__() self.embedding = PanguAlphaEmbedding(config) self.blocks = nn.CellList() self.use_past = config.use_past self.dtype = config.compute_dtype self.num_layers = config.num_layers self.is_pipeline = (config.stage_num > 1) if self.is_pipeline: self.top_query_embedding_table = Parameter(initializer(TruncatedNormal(0.02), [config.seq_length, config.embedding_size]), name='embedding_table', parallel_optimizer=False) self.top_query_embedding = EmbeddingLookup() for i in range(config.num_layers): if i == config.num_layers - 1: self.top_query_embedding_table.comm_fusion = 2 self.top_query_embedding_table.add_pipeline_stage(i * config.stage_num // config.num_layers) per_block = QueryLayer(config).set_comm_fusion(2) else: per_block = Decoder(config, i + 1).set_comm_fusion(2) per_block.pipeline_stage = i * config.stage_num // config.num_layers per_block.recompute() self.blocks.append(per_block) self.layernorm = LayerNorm((config.embedding_size,), config.dp).to_float(mstype.float32) self.layernorm.set_comm_fusion(2) self.layernorm.pipeline_stage = config.stage_num - 1 else: # The input_position representing the position ids will be used as the index # for a query embedding table to obtain top query hidden states, together with the previous outputs of normal # self-attention layers, a new attention layer will be attached to the output of the model if config.load_ckpt_path: # Loading the embedding table from the ckpt path: embedding_path = os.path.join(config.load_ckpt_path, 'top_query_embedding.npy') if os.path.exists(embedding_path): top_query_table = np.load(embedding_path) top_query_table_param = Tensor(top_query_table, mstype.float32) print("###### loading Toq_query embedding Table successed! ######") else: raise ValueError( f"{embedding_path} file not exits, please check whether top_query_embedding file exist.") else: top_query_table_param = TruncatedNormal(0.02) self.top_query_embedding_table = Parameter(initializer(top_query_table_param, [config.seq_length, config.embedding_size]), name='embedding_table', parallel_optimizer=False) self.top_query_embedding = EmbeddingLookup() # Total fusion groups for HCCL operators. Specifically, the same tyep HCCL operators in same group will be fused. fusion_group_num = 4 fusion_group_size = config.num_layers // fusion_group_num fusion_group_size = max(fusion_group_size, 1) for i in range(config.num_layers-1): per_block = Decoder(config, i + 1).set_comm_fusion(int(i / fusion_group_size) + 2) # Each layer will be remoputed in the backward process. The output activation of each layer will be saved, # in other words, in backward process each block will be almosttotally recomputed. per_block.recompute() self.blocks.append(per_block) self.layernorm = LayerNorm((config.embedding_size,), config.dp).to_float(mstype.float32) self.layernorm.set_comm_fusion(int((config.num_layers - 1) / fusion_group_size) + 2) self.top_query_layer = QueryLayer(config).set_comm_fusion( int((config.num_layers - 1) / fusion_group_size) + 2) self.top_query_layer.recompute() self.top_query_embedding_table.comm_fusion = int((config.num_layers - 1) / fusion_group_size) + 2 self.top_query_embedding.gather.shard(((1, 1), (config.dp,))) def construct(self, input_ids, input_mask, table, input_position, attention_mask, init_reset=True, batch_valid_length=None): """PanguAlpha model""" # embedding for input_ids and the lower triangle like attention_mask matrix hidden_states, attention_mask = self.embedding(input_ids, input_mask, table, input_position, attention_mask, batch_valid_length) for i in range(self.num_layers - 1): hidden_states, _ = self.blocks[i](hidden_states, attention_mask, init_reset, batch_valid_length) if self.is_pipeline: top_query_hidden_states = self.top_query_embedding(input_position.view(-1,), self.top_query_embedding_table) hidden_states, _ = self.blocks[self.num_layers - 1](hidden_states, top_query_hidden_states, attention_mask, init_reset, batch_valid_length) output_state = self.layernorm(hidden_states) output_state = F.cast(output_state, self.dtype) else: output_state = self.layernorm(hidden_states) output_state = F.cast(output_state, self.dtype) top_query_hidden_states = self.top_query_embedding(input_position.view(-1,), self.top_query_embedding_table) output_state, _ = self.top_query_layer(output_state, top_query_hidden_states, attention_mask, init_reset, batch_valid_length) return output_state class PanguAlpha_Head(nn.Cell): """ Head for PanguAlpha to get the logits of each token in the vocab Args: config(PanguAlphaConfig): the config of network Inputs: state: the output of the backbone embedding_table: the embedding table of the vocabulary Returns: logits: Tensor, the logits of the corresponding inputs """ def __init__(self, config): super(PanguAlpha_Head, self).__init__() if config.word_emb_dp: self.matmul = P.MatMul(transpose_b=True).shard(((config.dp, 1), (1, 1))) else: self.matmul = P.MatMul(transpose_b=True).shard(((config.dp, 1), (config.mp, 1))) self.embedding_size = config.embedding_size self.log_softmax = P.LogSoftmax(axis=-1) self.dtype = config.compute_dtype self.cast = P.Cast() def construct(self, state, embedding_table): state = P.Reshape()(state, (-1, self.embedding_size)) # output logits over vocabulary [bs*seq_length, vocab_size] logits = self.matmul(state, self.cast(embedding_table, self.dtype)) return logits class PanguAlpha(nn.Cell): """ The PanguAlpha network consisting of two parts the backbone and the head Args: config(PanguAlphaConfig): the config of network Inputs: input_ids: the tokenized inputs input_mask: the mask indicating whether each position is a valid input past: the previous feature map Returns: logits: Tensor: the logits of the corresponding inputs with shape (batch_size, seq_length, vocab_size) """ def __init__(self, config): super(PanguAlpha, self).__init__() # Network head to get logits over vocabulary self.head = PanguAlpha_Head(config) self.vocab_size = config.vocab_size self.embedding_size = config.embedding_size self.backbone = PanguAlpha_Model(config) if config.stage_num > 1: self.head.pipeline_stage = config.stage_num - 1 self.embedding_table = Parameter(initializer(Normal(0.02), [self.vocab_size, self.embedding_size]), name="embedding_table", parallel_optimizer=False) self.embedding_table.add_pipeline_stage(self.backbone.blocks[0].pipeline_stage) self.embedding_table.add_pipeline_stage(self.head.pipeline_stage) else: if config.load_ckpt_path: # Loading the embedding table from the ckpt path: embedding_path = os.path.join(config.load_ckpt_path, 'word_embedding.npy') if os.path.exists(embedding_path): e_table = np.load(embedding_path) e_table = Tensor(e_table, mstype.float32) self.embedding_table = Parameter(e_table, name="embedding_table", parallel_optimizer=False) print("###### loading Word embedding Table successed! ######") else: raise ValueError(f"{embedding_path} file not exits, " f"please check whether word_embedding file exist.") else: self.embedding_table = Parameter(initializer(Normal(0.02), [self.vocab_size, self.embedding_size]), name="embedding_table", parallel_optimizer=False) def construct(self, input_ids, input_mask, input_position, attention_mask, init_reset=True, batch_valid_length=None): output_states = self.backbone(input_ids, input_mask, self.embedding_table, input_position, attention_mask, init_reset, batch_valid_length) logits = self.head(output_states, self.embedding_table) return logits class CrossEntropyLoss(nn.Cell): """ Calculate the cross entropy loss Args: config(PanguAlphaConfig): the config of the network Inputs: logits: the output logits of the backbone label: the ground truth label of the sample input_mask: the mask indicating whether each position is a valid input Returns: loss: Tensor, the corrsponding cross entropy loss """ def __init__(self, config): super(CrossEntropyLoss, self).__init__() self.mean = P.ReduceMean() self.sum = P.ReduceSum().shard(((config.dp, config.mp),)) self.onehot = P.OneHot().shard(((config.dp, config.mp), (), ())) # on/off value for onehot, for smooth labeling, modify the off_value self.on_value = Tensor(1.0, mstype.float32) self.off_value = Tensor(0.0, mstype.float32) self.vocab_size = config.vocab_size self.max = P.ArgMaxWithValue(axis=-1, keep_dims=True).shard( ((config.dp, config.mp),)) self.eps_const = Tensor(1e-24, mstype.float32) self.sub = P.Sub().shard(((config.dp, config.mp), (config.dp, 1))) self.exp = P.Exp().shard(((config.dp, config.mp),)) self.div = P.RealDiv().shard(((config.dp, config.mp), (config.dp, 1))) self.log = P.Log().shard(((config.dp, config.mp),)) self.add = P.TensorAdd().shard(((config.dp, config.mp), ())) self.mul = P.Mul().shard( ((config.dp, config.mp), (config.dp, config.mp))) self.neg = P.Neg().shard(((config.dp, config.mp),)) self.sum2 = P.ReduceSum().shard(((1,),)) self.mul2 = P.Mul().shard(((1,), (1,))) self.add2 = P.TensorAdd() self.div2 = P.RealDiv() def construct(self, logits, label, input_mask): r""" Compute loss using logits, label and input mask """ # [bs*seq_length, vocab_size] logits = F.cast(logits, mstype.float32) # LogSoftmax for logits over last dimension _, logit_max = self.max(logits) logit_sub = self.sub(logits, logit_max) logit_exp = self.exp(logit_sub) exp_sum = self.sum(logit_exp, -1) exp_sum = P.Reshape()(exp_sum, (F.shape(exp_sum)[0], 1)) softmax_result = self.div(logit_exp, exp_sum) log_softmax_result = self.log(self.add(softmax_result, self.eps_const)) # Flatten label to [bs*seq_length] label = P.Reshape()(label, (-1,)) # Get onehot label [bs*seq_length, vocab_size] one_hot_label = self.onehot(label, self.vocab_size, self.on_value, self.off_value) # Cross-Entropy loss loss = self.mul(log_softmax_result, one_hot_label) loss_unsum = self.neg(loss) loss_reduce = self.sum(loss_unsum, -1) # input_mask indicates whether there is padded inputs and for padded inputs it will not be counted into loss input_mask = P.Reshape()(input_mask, (-1,)) numerator = self.sum2(self.mul2(loss_reduce, input_mask)) denominator = self.add2( self.sum2(input_mask), P.Cast()(F.tuple_to_array((1e-5,)), mstype.float32)) loss = self.div2(numerator, denominator) return loss class PanguAlphaWithLoss(nn.Cell): """ PanguAlpha training loss Args: network: backbone network of PanguAlpha loss: loss function, e.g., crossentropy eos_token: the end_of_sentence token Inputs: input_ids: the tokenized inputs past: the previous feature map Returns: output: Tensor, the loss of the network """ def __init__(self, config, network, loss, eos_token=6): super(PanguAlphaWithLoss, self).__init__(auto_prefix=False) self.network = network self.loss = loss self.eos_token = eos_token self.slice = P.StridedSlice().shard(((config.dp, 1),)) self.not_equal = P.NotEqual().shard(((config.dp, 1), ())) self.batch_size = config.batch_size self.len = config.seq_length self.micro_batch_step = 1 if config.stage_num > 1: self.micro_batch_step = config.micro_size def construct(self, input_ids, input_position, attention_mask): tokens = self.slice(input_ids, (0, 0), (self.batch_size // self.micro_batch_step, -1), (1, 1)) input_mask = F.cast(self.not_equal(tokens, self.eos_token), mstype.float32) # input_position = self.slice(input_position, (0, 0), (self.batch_size, self.len), (1, 1)) # attention_mask = self.slice_mask(attention_mask, (0, 0, 0), # (self.batch_size, self.len, self.len), # (1, 1, 1)) logits = self.network(tokens, input_mask, input_position, attention_mask) labels = self.slice(input_ids, (0, 1), (self.batch_size // self.micro_batch_step, self.len + 1), (1, 1)) output = self.loss(logits, labels, input_mask) return output class PanguAlphaWithLoss_eval_nli(nn.Cell): """ PanguAlpha training loss Args: network: backbone network of PanguAlpha loss: loss function, e.g., crossentropy eos_token: the end_of_sentence token Inputs: input_ids: the tokenized inputs past: the previous feature map Returns: output: Tensor, the loss of the network """ def __init__(self, config, network, loss, eos_token=6): super(PanguAlphaWithLoss_eval_nli, self).__init__(auto_prefix=False) self.network = network self.loss = loss self.eos_token = eos_token self.slice = P.StridedSlice().shard(((config.dp, 1),)) self.not_equal = P.NotEqual().shard(((config.dp, 1), ())) self.batch_size = config.batch_size self.len = config.seq_length self.micro_batch_step = 1 if config.stage_num > 1: self.micro_batch_step = config.micro_size def construct(self, input_ids, input_position=None, attention_mask=None): tokens = self.slice(input_ids, (0, 0), (self.batch_size // self.micro_batch_step, -1), (1, 1)) input_mask = F.cast(self.not_equal(tokens, self.eos_token), mstype.float32) input_position = self.slice(input_position, (0, 0), (self.batch_size, self.len), (1, 1)) attention_mask = self.slice_mask(attention_mask, (0, 0, 0), (self.batch_size, self.len, self.len), (1, 1, 1)) logits = self.network(tokens, input_mask, input_position, attention_mask) labels = self.slice(input_ids, (0, 1), (self.batch_size // self.micro_batch_step, self.len + 1), (1, 1)) output = self.loss(logits, labels, input_mask) return output class PanguAlphaWithLoss_eval(nn.Cell): """ GPT training loss Args: network: backbone network of GPT2/3 loss: loss function, e.g., crossentropy eos_token: the end_of_sentence token Inputs: input_ids: the tokenized inputs past: the previous feature map Returns: output: Tensor, the loss of the network """ def __init__(self, config, network, loss, eos_token=128297): super(PanguAlphaWithLoss_eval, self).__init__(auto_prefix=False) self.network = network self.loss = loss self.eos_token = eos_token self.slice = P.StridedSlice().shard(((config.dp, 1),)) self.not_equal = P.NotEqual().shard(((config.dp, 1), ())) self.batch_size = config.batch_size self.len = config.seq_length self.slice_mask = P.StridedSlice().shard(((config.dp, 1, 1),)) def construct(self, input_ids, mask_ids_input, input_position=None, attention_mask=None): #tokens = input_ids[:, :-1] tokens = self.slice(input_ids, (0, 0), (self.batch_size, -1), (1, 1)) input_position = self.slice(input_position, (0, 0), (self.batch_size, self.len), (1, 1)) attention_mask = self.slice_mask(attention_mask, (0, 0, 0), (self.batch_size, self.len, self.len), (1, 1, 1)) input_mask = F.cast(self.not_equal(tokens, self.eos_token), mstype.float32) logits = self.network(tokens, input_mask, input_position, attention_mask) #labels = input_ids[:, 1:] labels = self.slice(input_ids, (0, 1), (self.batch_size, self.len + 1), (1, 1)) output = self.loss(logits, labels, mask_ids_input) return output class AttentionMask(nn.Cell): """ Get the attention matrix for self-attention module Args: seq_length: the pre-defined sequence length Inputs: input_mask: the mask indicating whether each position is a valid input Returns: attention_mask: the attention mask matrix with shape (batch_size, seq_length, seq_length) """ def __init__(self, seq_length): super(AttentionMask, self).__init__() self.reshape = P.Reshape() self.mul = P.BatchMatMul().shard( ((1, 1, 1), (1, 1, 1))) self.expand_dim = P.ExpandDims().shard(((1, 1),)) ones = np.ones(shape=(seq_length, seq_length)) self.lower_triangle_mask = Tensor(np.tril(ones), mstype.float32) self.multiply = P.Mul().shard(((1, 1, 1), (1, 1, 1))) def construct(self, input_mask): input_shape = P.Shape()(input_mask) shape_right = (input_shape[0], 1, input_shape[1]) shape_left = input_shape + (1,) mask_left = self.reshape(input_mask, shape_left) mask_right = self.reshape(input_mask, shape_right) attention_mask = self.mul(mask_left, mask_right) lower_triangle = self.expand_dim(self.lower_triangle_mask, 0) attention_mask = self.multiply(attention_mask, lower_triangle) return attention_mask class EvalNet(nn.Cell): """ PanguAlpha evaluation net Args: backbone: backbone network of PanguAlpha generate: enable generate mode Inputs: input_ids: the tokenized inpus current_index: the index of current token init_reset: whether reset saved states Returns: outputs: Tensor, corresponding output for different tasks """ def __init__(self, backbone, generate=False, pad_token=6, seq_length=1024): super(EvalNet, self).__init__(auto_prefix=False) self.backbone = backbone self.pad_token = pad_token self.argmax = P.Argmax() self.generate = generate self.topk = P.TopK(sorted=True).shard(((1, 1),)) self.gather = P.GatherV2().shard(((1, 1), (1,))) self.log_softmax = P.LogSoftmax().shard(((1, 1, 1),)) self.get_attention_mask = AttentionMask(seq_length) def construct(self, input_ids, current_index, init_reset=True, batch_valid_length=None): """evaluation net""" input_mask = F.cast(F.not_equal(input_ids, self.pad_token), mstype.float32) bs, seq_length = F.shape(input_ids) attention_mask = self.get_attention_mask(input_mask) input_position = F.tuple_to_array(F.make_range(seq_length)) input_position = P.Tile()(input_position, (bs, 1)) logits = self.backbone(input_ids, input_mask, input_position, attention_mask, init_reset, batch_valid_length) index = current_index.view(1,) logits = self.gather(logits, index, 0) logits = logits.view(bs, 1, -1) log_probs = self.log_softmax(logits) return log_probs pcl_pangu/pcl_pangu/model/panguAlpha_mindspore/src/pangu_alpha_config.py000066400000000000000000000142671443434453700272300ustar00rootroot00000000000000# Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """ network config setting """ import mindspore.common.dtype as mstype class PANGUALPHAConfig: """ PANGUALPHA config class which defines the model size """ def __init__(self, data_parallel_num, model_parallel_num, batch_size=30, seq_length=1024, vocab_size=40000, embedding_size=768, num_layers=12, num_heads=12, expand_ratio=4, post_layernorm_residual=False, dropout_rate=0.1, compute_dtype=mstype.float16, use_past=False, self_layernorm=True, word_emb_dp=True, stage_num=16, eod_reset=True, micro_size=32, load_ckpt_path='/cache/ckpt_file', use_top_query_attention=False, param_init_type=mstype.float32): self.batch_size = batch_size self.seq_length = seq_length self.vocab_size = vocab_size self.embedding_size = embedding_size self.num_layers = num_layers self.num_heads = num_heads # The expand ratio of feature size in FFN self.expand_ratio = expand_ratio # Use post-layernorm or pre-layernrom, default:pre-layernorm self.post_layernorm_residual = post_layernorm_residual self.dropout_rate = dropout_rate self.compute_dtype = compute_dtype # Whether use incremental inference self.use_past = use_past self.dp = data_parallel_num self.mp = model_parallel_num self.self_layernorm = self_layernorm self.stage_num = stage_num self.micro_size = micro_size self.word_emb_dp = word_emb_dp self.eod_reset = eod_reset self.use_top_query_attention = use_top_query_attention self.param_init_type = param_init_type # Used for loading embedding tables self.load_ckpt_path = load_ckpt_path def __str__(self): info = "[PANGUALPHAConfig]" + '===' * 10 + '\n' for k, v in self.__dict__.items(): var_info = "{}:{}\n".format(k, v) info += var_info info += '=' * 10 return info def set_parse(args_opt): r""" Set config according to the mode """ if args_opt.model == "200B": args_opt.embedding_size = 16384 args_opt.num_layers = 64 args_opt.num_heads = 128 if args_opt.per_batch_size == 0: args_opt.per_batch_size = 1 args_opt.word_emb_dp = 0 if args_opt.run_type == "train": args_opt.start_lr = 6e-5 args_opt.end_lr = 6e-6 args_opt.stage_num = 16 args_opt.micro_size = 32 args_opt.op_level_model_parallel_num = 16 if args_opt.optimizer_shard == 1: args_opt.op_level_model_parallel_num = 8 elif args_opt.run_type == "predict": args_opt.stage_num = 4 args_opt.micro_size = 1 args_opt.op_level_model_parallel_num = 16 if args_opt.optimizer_shard == 1: args_opt.op_level_model_parallel_num = 8 elif args_opt.model == "13B": args_opt.embedding_size = 5120 args_opt.num_layers = 40 args_opt.num_heads = 40 args_opt.word_emb_dp = 1 args_opt.op_level_model_parallel_num = 8 if args_opt.run_type == "train": args_opt.start_lr = 1e-4 args_opt.end_lr = 1e-6 args_opt.optimizer_shard = 1 args_opt.stage_num = 1 args_opt.micro_size = 1 args_opt.full_batch = 0 if args_opt.per_batch_size == 0: args_opt.per_batch_size = 2 elif args_opt.run_type == "predict": args_opt.stage_num = 1 args_opt.micro_size = 1 if args_opt.per_batch_size == 0: args_opt.per_batch_size = 1 elif args_opt.model == "2B6": args_opt.embedding_size = 2560 # 2560 args_opt.num_layers = 32 # 32 args_opt.num_heads = 32 # 32 args_opt.op_level_model_parallel_num = 8 if args_opt.run_type == "train": args_opt.start_lr = 1e-4 args_opt.end_lr = 1e-6 args_opt.optimizer_shard = 1#1 args_opt.stage_num = 1 args_opt.micro_size = 1 args_opt.full_batch = 0 # 1 if args_opt.per_batch_size == 0: args_opt.per_batch_size = 4## 8 elif args_opt.run_type == "pre_trained":#继续训练，持续学习 args_opt.pre_trained = True args_opt.start_lr = 2e-5 args_opt.end_lr = 1e-6 args_opt.optimizer_shard = 1 args_opt.stage_num = 1 args_opt.micro_size = 1 args_opt.full_batch = 0 if args_opt.per_batch_size == 0: args_opt.per_batch_size = 16 elif args_opt.run_type == "incremental_train": args_opt.start_lr = 1e-5 ## 1e-4 args_opt.end_lr = 1e-6 ## 1e-6 args_opt.optimizer_shard = 0 args_opt.stage_num = 1 args_opt.micro_size = 1 args_opt.full_batch = 0 if args_opt.per_batch_size == 0: args_opt.per_batch_size = 6## 8 elif args_opt.run_type == "predict": args_opt.op_level_model_parallel_num = 1 args_opt.stage_num = 1 args_opt.micro_size = 1 if args_opt.per_batch_size == 0: args_opt.per_batch_size = 1 pcl_pangu/pcl_pangu/model/panguAlpha_mindspore/src/pangu_alpha_wrapcell.py000066400000000000000000000273721443434453700275750ustar00rootroot00000000000000# Copyright 2022 PCL, Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """GPT training wrapper""" import mindspore.nn as nn from mindspore.ops import operations as P from mindspore.ops import composite as C from mindspore.ops import functional as F from mindspore.common.tensor import Tensor import mindspore.common.dtype as mstype from mindspore.nn.wrap.loss_scale import TrainOneStepWithLossScaleCell from mindspore import context, Parameter from mindspore.context import ParallelMode from mindspore.nn.wrap.grad_reducer import DistributedGradReducer from mindspore.communication.management import get_group_size from mindspore.parallel._utils import _get_enable_parallel_optimizer from src.utils_pangu import ClipByGlobalNorm GRADIENT_CLIP_TYPE = 1 GRADIENT_CLIP_VALUE = 1.0 clip_grad = C.MultitypeFuncGraph("clip_grad") @clip_grad.register("Number", "Number", "Tensor") def _clip_grad(clip_type, clip_value, grad): """ Clip gradients. Inputs: clip_type (int): The way to clip, 0 for 'value', 1 for 'norm'. clip_value (float): Specifies how much to clip. grad (tuple[Tensor]): Gradients. Outputs: tuple[Tensor], clipped gradients. """ if clip_type not in [0, 1]: return grad dt = F.dtype(grad) # 0 for clip_by_value and 1 for clip_by_norm if clip_type == 0: new_grad = C.clip_by_value( grad, F.cast(F.tuple_to_array((-clip_value,)), dt), F.cast(F.tuple_to_array((clip_value,)), dt)) else: new_grad = nn.ClipByNorm()(grad, F.cast(F.tuple_to_array((clip_value,)), dt)) return new_grad grad_scale = C.MultitypeFuncGraph("grad_scale") shard_grad_scale = C.MultitypeFuncGraph("shard_grad_scale") reciprocal = P.Reciprocal() @grad_scale.register("Tensor", "Tensor") def tensor_grad_scale(scale, grad): return grad * reciprocal(scale) @grad_scale.register("Tensor", "Tensor", "Tensor") def tensor_grad_scale_pipeline(scale, grad, accu_grad): accu_grad = F.depend(accu_grad, grad) new_grad = accu_grad * reciprocal(scale) accu_grad = F.depend(accu_grad, new_grad) zeros = F.tensor_mul(accu_grad, 0.0) new_grad = F.depend(new_grad, F.assign(accu_grad, zeros)) return new_grad @shard_grad_scale.register("Tensor", "Tensor", "Tensor") def tensor_shard_grad_scale_pipeline(scale, grad, accu_grad): new_grad = grad * reciprocal(scale) accu_grad = F.depend(accu_grad, new_grad) new_grad = F.depend(new_grad, F.assign(accu_grad, F.zeros_like(accu_grad))) return new_grad class PanguAlphaTrainOneStepWithLossScaleCell(TrainOneStepWithLossScaleCell): """ Encapsulation class of PanguAlpha network training. Append an optimizer to the training network after that the construct function can be called to create the backward graph. Args: network (Cell): The training network. Note that loss function should have been added. optimizer (Optimizer): Optimizer for updating the weights. scale_update_cell (Cell): Cell to do the loss scale. Default: None. """ def __init__(self, network, optimizer, scale_update_cell=None, enable_global_norm=False, config=None): super(PanguAlphaTrainOneStepWithLossScaleCell, self).__init__(network, optimizer, scale_update_cell) self.network = network self.config = config self.weights = optimizer.parameters self.optimizer = optimizer self.default_lr = Tensor([0.0], dtype=mstype.float32) self.enable_global_norm = enable_global_norm self.clip = ClipByGlobalNorm(self.weights, config) self.cast = P.Cast() ## self.alloc_status = P.NPUAllocFloatStatus().add_prim_attr("_side_effect_flag", False) ##self.get_status = P.NPUGetFloatStatus().add_prim_attr("_side_effect_flag", False) ##self.clear_before_grad = P.NPUClearFloatStatus().add_prim_attr("_side_effect_flag", False) def construct(self, input_ids, input_position, attention_mask, layer_past=None, sens=None): """Defines the computation performed.""" weights = self.weights # Forward process loss = self.network(input_ids, input_position, attention_mask) scaling_sens = self.scale_sense # alloc status and clear should be right before gradoperation ############################################################################ ##init = self.alloc_status() ##status_clear = self.clear_before_grad(init) ##loss = F.depend(loss, status_clear) ############################################################################ status, scaling_sens = self.start_overflow_check(loss, scaling_sens) scaling_sens_filled = C.ones_like(loss) * F.cast(scaling_sens, F.dtype(loss)) # Backward process using loss scale grads = self.grad(self.network, weights)(input_ids, input_position, attention_mask, scaling_sens_filled) # apply grad reducer on grads grads = self.grad_reducer(grads) grads = self.hyper_map( F.partial(grad_scale, scaling_sens), grads) if self.enable_global_norm: grads, _ = self.clip(grads) else: grads = self.hyper_map( F.partial(clip_grad, GRADIENT_CLIP_TYPE, GRADIENT_CLIP_VALUE), grads) # Check whether overflow cond = self.get_overflow_status(status, grads) overflow = self.process_loss_scale(cond) # If overflow, surpass weights update # if not, update weights if overflow: succ = False ## lr = self.default_lr return F.depend(loss, succ), cond, scaling_sens, self.default_lr else: succ, lr = self.optimizer(grads) ##succ = self.optimizer(grads) return F.depend(loss, succ), cond, scaling_sens, lr class PanguAlphaTrainPipelineWithLossScaleCell(nn.Cell): """ Encapsulation class of PanguAlpha network training. Append an optimizer to the training network after that the construct function can be called to create the backward graph. Args: network (Cell): The training network. Note that loss function should have been added. optimizer (Optimizer): Optimizer for updating the weights. scale_update_cell (Cell): Cell to do the loss scale. Default: None. """ def __init__(self, network, optimizer, config, scale_update_cell=None, enable_global_norm=True): super(PanguAlphaTrainPipelineWithLossScaleCell, self).__init__(auto_prefix=False) self.config = config self.network = network self.network.add_flags(defer_inline=True) self.weights = optimizer.parameters self.accu_grads = self.weights.clone(prefix="accu_grads", init="zeros") self.optimizer = optimizer self.enable_global_norm = enable_global_norm self.grad = C.GradOperation(get_by_list=True, sens_param=True) self.reducer_flag = False self.allreduce = P.AllReduce() self.parallel_mode = context.get_auto_parallel_context("parallel_mode") if self.parallel_mode in [ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL]: self.reducer_flag = True self.grad_reducer = F.identity self.degree = 1 if self.reducer_flag: self.degree = get_group_size() self.grad_reducer = DistributedGradReducer(optimizer.parameters, False, self.degree) self.is_distributed = (self.parallel_mode != ParallelMode.STAND_ALONE) self.cast = P.Cast() self.alloc_status = P.NPUAllocFloatStatus().add_prim_attr("_side_effect_flag", False) self.get_status = P.NPUGetFloatStatus().add_prim_attr("_side_effect_flag", False) self.clear_before_grad = P.NPUClearFloatStatus().add_prim_attr("_side_effect_flag", False) self.reduce_sum = P.ReduceSum(keep_dims=False) self.base = Tensor(1, mstype.float32) self.less_equal = P.LessEqual() self.hyper_map = C.HyperMap() self.loss_scale = None self.reshape = P.Reshape() self.loss_scaling_manager = scale_update_cell if scale_update_cell: self.loss_scale = Parameter(Tensor(scale_update_cell.get_loss_scale(), dtype=mstype.float32), name="loss_scale") self.clip = ClipByGlobalNorm(self.weights, self.config) self.micro_size = config.micro_size self.opt_shard = _get_enable_parallel_optimizer() @C.add_flags(has_effect=True) def construct(self, input_ids, input_position, attention_mask, past=None, sens=None): """Defines the computation performed.""" weights = self.weights loss = self.network(input_ids, input_position, attention_mask) if sens is None: scaling_sens = self.loss_scale scaling_sens = self.reshape(scaling_sens, (1,)) else: scaling_sens = sens # alloc status and clear should be right before gradoperation init = self.alloc_status() status_clear = self.clear_before_grad(init) grads = self.grad(self.network, weights)(input_ids, input_position, attention_mask, self.cast(scaling_sens / self.micro_size, mstype.float32)) init = F.depend(init, grads) get_status = self.get_status(init) init = F.depend(init, get_status) flag_sum = self.reduce_sum(init, (0,)) loss = F.depend(loss, status_clear) # apply grad reducer on grads if self.opt_shard: grads = self.grad_reducer(grads) grads = self.hyper_map(F.partial(shard_grad_scale, scaling_sens * self.degree), grads, self.accu_grads) else: accu_grads = self.grad_reducer(self.accu_grads) grads = self.hyper_map(F.partial(grad_scale, scaling_sens * self.degree), grads, accu_grads) if self.enable_global_norm: grads, _ = self.clip(grads) else: grads = self.hyper_map( F.partial(clip_grad, GRADIENT_CLIP_TYPE, GRADIENT_CLIP_VALUE), grads) if self.is_distributed: # sum overflow flag over devices flag_reduce = self.allreduce(flag_sum) cond = self.less_equal(self.base, flag_reduce) else: cond = self.less_equal(self.base, flag_sum) overflow = cond if sens is None: overflow = self.loss_scaling_manager(self.loss_scale, cond) if overflow: succ = False ret = (loss, overflow, scaling_sens, self.default_lr) else: succ, lr = self.optimizer(grads) ## succ = self.optimizer(grads) ret = (loss, overflow, scaling_sens, lr) return F.depend(ret, succ) pcl_pangu/pcl_pangu/model/panguAlpha_mindspore/src/preprocess.py000066400000000000000000000214211443434453700255770ustar00rootroot00000000000000# Copyright 2022 PCL, Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """ transform wikitext-2, wikitext-103, lambada, openwebtext dataset to mindrecord. """ import argparse import glob import json import os import re from functools import partial from multiprocessing import Pool, current_process import numpy as np from mindspore.mindrecord import FileWriter from src.tokenization_jieba import JIEBATokenizer def chunks(lst, n): """ yield n sized chunks from list""" for i in range(0, len(lst), n): yield lst[i:i+n] def package_file(it, n): """ package multiple files""" stop = False while not stop: batch = [] for _ in range(n): try: batch.append(next(it)) except StopIteration: stop = True if not batch: break yield batch def clean_wikitext(string): """ cleaning wikitext dataset""" # contractions string = string.replace("s '", "s'") string = re.sub(r"/' [0-9]/", r"/'[0-9]/", string) # number separators string = string.replace(" @-@ ", "-") string = string.replace(" @,@ ", ",") string = string.replace(" @.@ ", ".") # punctuation string = string.replace(" : ", ": ") string = string.replace(" ; ", "; ") string = string.replace(" . ", ". ") string = string.replace(" ! ", "! ") string = string.replace(" ? ", "? ") string = string.replace(" , ", ", ") # double brackets string = re.sub(r"\(\s*([^\)]*?)\s*\)", r"(\1)", string) string = re.sub(r"\[\s*([^\]]*?)\s*\]", r"[\1]", string) string = re.sub(r"{\s*([^}]*?)\s*}", r"{\1}", string) string = re.sub(r"\"\s*([^\"]*?)\s*\"", r'"\1"', string) string = re.sub(r"'\s*([^']*?)\s*'", r"'\1'", string) # miscellaneous string = string.replace("= = = =", "====") string = string.replace("= = =", "===") string = string.replace("= =", "==") string = string.replace(" "+chr(176)+" ", chr(176)) string = string.replace(" \n", "\n") string = string.replace("\n ", "\n") string = string.replace(" N ", " 1 ") string = string.replace(" 's", "'s") return string def tokenize_openwebtext(tokenizer, iterator, seq_length, eot): """ tokenize openwebtext dataset""" for file_path in iterator: if os.path.getsize(file_path) == 0: continue content = [] with open(file_path, 'r', encoding='utf-8') as f: for para in f.read().split("\n\n"): if para: tokenized_text = tokenizer.tokenize(para) content += tokenizer.convert_tokens_to_ids(tokenized_text) + [ eot] for chunk in chunks(content, seq_length): sample = {} if len(chunk) == seq_length: sample['input_ids'] = np.array(chunk, dtype=np.int32) yield sample def tokenize_wiki(tokenizer, file_path, seq_length, eot): """tokenize wikitext-2/wikitext-103 dataset""" content = [] with open(file_path, 'r', encoding='utf-8') as f: for para in clean_wikitext(f.read()).split("\n\n"): if para and para.strip().startswith('=') is False: tokenized_text = tokenizer.tokenize(para) content += tokenizer.convert_tokens_to_ids(tokenized_text) + [ eot] for chunk in chunks(content, seq_length): sample = {} if len(chunk) == seq_length: sample['input_ids'] = np.array(chunk, dtype=np.int32) yield sample def tokenize_lambada(tokenizer, file_path, seq_length, eot): """tokenize lambada dataset""" content = [] with open(file_path, 'r', encoding='utf-8') as f: for line in f.readlines(): para = json.loads(line)['text'].replace( "“", '"').replace("”", '"').strip().strip(".") tokenized_text = tokenizer.tokenize(para) content += tokenizer.convert_tokens_to_ids(tokenized_text) + [eot] for chunk in chunks(content, seq_length): sample = {} if len(chunk) == seq_length: sample['input_ids'] = np.array(chunk, dtype=np.int32) yield sample def task_unit(iterator, tokenizer, seq_length, eot, parallel_writer=True): """task for each process""" p = current_process() index = p.pid if p.pid else 0 item_iter = tokenize_openwebtext(tokenizer, iterator, seq_length, eot) batch_size = 1024 # size of write batch count = 0 while True: data_batch = [] try: for _ in range(batch_size): data_batch.append(next(item_iter)) count += 1 writer.write_raw_data(data_batch, parallel_writer=parallel_writer) print("Process {} transformed {} records.".format( index, count)) except StopIteration: if data_batch: writer.write_raw_data(data_batch, parallel_writer=parallel_writer) print("Process {} transformed {} records.".format( index, count)) break if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--dataset_type', type=str, default='openwebtext') parser.add_argument('--input_glob', type=str, default='*.txt') parser.add_argument('--output_file', type=str, default='./output/transfered_mindrecord') parser.add_argument('--tokenizer', type=str, default='gpt', choices=['gpt', 'jieba']) parser.add_argument('--vocab_file', type=str, default=None) parser.add_argument('--model_file', type=str, default=None) parser.add_argument('--file_partition', type=int, default=1) parser.add_argument('--file_batch_size', type=int, default=1024) parser.add_argument('--num_process', type=int, default=64) parser.add_argument('--seq_length', type=int, default=1025) parser.add_argument('--eot', type=int, default=50256) parser.add_argument('--data_column_name', type=str, default='input_ids') args = parser.parse_args() out_dir, out_file = os.path.split(os.path.abspath(args.output_file)) if not os.path.exists(out_dir): os.mkdir(out_dir) schema = {args.data_column_name: {"type": "int32", "shape": [-1]},} writer = FileWriter(file_name=args.output_file, shard_num=args.file_partition) writer.add_schema(schema, args.dataset_type) writer.open_and_set_header() # Start to load tokenizer if args.tokenizer == 'gpt': try: from transformers import GPT2Tokenizer except ModuleNotFoundError: print("module 'transformers' not installed.") word_tokenizer = GPT2Tokenizer.from_pretrained("gpt2") else: if not os.path.exists(args.vocab_file): raise FileNotFoundError(f"file {args.vocab_file} do not exists.") if not os.path.exists(args.model_file): raise FileNotFoundError(f"file {args.model_file} do not exists.") word_tokenizer = JIEBATokenizer(vocab_file=args.vocab_file, model_file=args.model_file) transforms_count = 0 if args.dataset_type == 'wiki': for x in tokenize_wiki(word_tokenizer, args.input_glob, args.seq_length, args.eot): transforms_count += 1 writer.write_raw_data([x]) print("Transformed {} records.".format(transforms_count)) elif args.dataset_type == 'lambada': for x in tokenize_lambada(word_tokenizer, args.input_glob, args.seq_length, args.eot): transforms_count += 1 writer.write_raw_data([x]) print("Transformed {} records.".format(transforms_count)) elif args.dataset_type == 'openwebtext': file_iter = glob.iglob(args.input_glob) with Pool(processes=args.num_process) as pool: map_func = partial(task_unit, tokenizer=word_tokenizer, seq_length=args.seq_length, eot=args.eot) pool.map(map_func, package_file(file_iter, args.file_batch_size)) else: raise ValueError( "Not support dataset type: {}".format(args.dataset_type)) writer.commit() out_file = args.output_file if args.file_partition > 1: out_file += '0' print("Transform finished, output files refer: {}".format(out_file)) pcl_pangu/pcl_pangu/model/panguAlpha_mindspore/src/serialization.py000066400000000000000000002036271443434453700263010ustar00rootroot00000000000000# Copyright 2022 PCL, Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """Model and parameters serialization.""" import os import sys import stat import math import shutil import time import copy import threading from threading import Thread, Lock from collections import defaultdict import numpy as np import mindspore.nn as nn import mindspore.context as context from mindspore import log as logger from mindspore.train.checkpoint_pb2 import Checkpoint from mindspore.train.print_pb2 import Print from mindspore.train.node_strategy_pb2 import ParallelStrategyMap, ParallelLayouts from mindspore.train.mind_ir_pb2 import ModelProto as mindir_model from mindspore.train.mind_ir_pb2 import GraphProto as graph_proto from mindspore.common.tensor import Tensor from mindspore.common.initializer import initializer from mindspore.common.parameter import Parameter from mindspore.common.api import _executor from mindspore.common import dtype as mstype from mindspore._checkparam import check_input_data, Validator from mindspore.compression.export import quant_export from mindspore.parallel._tensor import _load_tensor, _get_tensor_strategy, _get_tensor_slice_index from mindspore.parallel._utils import _infer_rank_list, _remove_repeated_slices from mindspore.communication.management import get_rank, get_group_size from mindspore._c_expression import load_mindir, _encrypt, _decrypt, _is_cipher_file tensor_to_ms_type = {"Int8": mstype.int8, "Uint8": mstype.uint8, "Int16": mstype.int16, "Uint16": mstype.uint16, "Int32": mstype.int32, "Uint32": mstype.uint32, "Int64": mstype.int64, "Uint64": mstype.uint64, "Float16": mstype.float16, "Float32": mstype.float32, "Float64": mstype.float64, "Bool": mstype.bool_} tensor_to_np_type = {"Int8": np.int8, "Uint8": np.uint8, "Int16": np.int16, "Uint16": np.uint16, "Int32": np.int32, "Uint32": np.uint32, "Int64": np.int64, "Uint64": np.uint64, "Float16": np.float16, "Float32": np.float32, "Float64": np.float64, "Bool": np.bool_} _ckpt_mutex = Lock() # unit is KB SLICE_SIZE = 512 * 1024 PROTO_LIMIT_SIZE = 1024 * 1024 * 2 TOTAL_SAVE = 1024 * 1024 def _special_process_par(par, new_par): """ Processes the special condition. Like (12,2048,1,1)->(12,2048), this case is caused by GE 4 dimensions tensor. """ par_shape_len = len(par.data.shape) new_par_shape_len = len(new_par.data.shape) if new_par_shape_len <= par_shape_len: return False for i in range(new_par_shape_len - par_shape_len): if new_par.data.shape[par_shape_len + i] != 1: return False new_val = new_par.data.asnumpy() new_val = new_val.reshape(par.data.shape) par.set_data(Tensor(new_val, par.data.dtype)) return True def _update_param(param, new_param, strict_load): """Updates param's data from new_param's data.""" if isinstance(param.data, Tensor) and isinstance(new_param.data, Tensor): if param.data.shape != new_param.data.shape: if not _special_process_par(param, new_param): logger.error("Failed to combine the net and the parameters for param %s.", param.name) msg = ("Net parameters {} shape({}) different from parameter_dict's({})" .format(param.name, param.data.shape, new_param.data.shape)) raise RuntimeError(msg) if param.data.dtype != new_param.data.dtype: if _type_convert(param, new_param, strict_load): new_tensor = Tensor(new_param.data.asnumpy(), param.data.dtype) param.set_data(new_tensor) return logger.error("Failed to combine the net and the parameters for param %s.", param.name) msg = ("Net parameters {} type({}) different from parameter_dict's({})" .format(param.name, param.data.dtype, new_param.data.dtype)) raise RuntimeError(msg) param.set_data(new_param.data, param.sliced) return if isinstance(param.data, Tensor) and not isinstance(new_param.data, Tensor): if param.data.shape != (1,) and param.data.shape != (): logger.error("Failed to combine the net and the parameters for param %s.", param.name) msg = ("Net parameters {} shape({}) is not (1,), inconsistent with parameter_dict's(scalar)." .format(param.name, param.data.shape)) raise RuntimeError(msg) param.set_data(initializer(new_param.data, param.data.shape, param.data.dtype)) elif isinstance(new_param.data, Tensor) and not isinstance(param.data, Tensor): logger.error("Failed to combine the net and the parameters for param %s.", param.name) msg = ("Net parameters {} type({}) different from parameter_dict's({})" .format(param.name, type(param.data), type(new_param.data))) raise RuntimeError(msg) else: param.set_data(type(param.data)(new_param.data)) def _type_convert(param, new_param, strict_load): """Whether to convert parameter's type during load checkpoint into network.""" float_type = (mstype.float16, mstype.float32, mstype.float64) int_type = (mstype.int8, mstype.int16, mstype.int32, mstype.int64) if not strict_load and ({param.data.dtype, new_param.data.dtype}.issubset(float_type) or {param.data.dtype, new_param.data.dtype}.issubset(int_type)): logger.warning("ckpt_dict parameter: {}'s type is {}, convert to {} in the network." .format(new_param.name, new_param.data.dtype, param.data.dtype)) return True return False def _exec_save(ckpt_file_name, data_list, enc_key=None, enc_mode="AES-GCM"): """Execute the process of saving checkpoint into file.""" try: with _ckpt_mutex: if os.path.exists(ckpt_file_name): os.remove(ckpt_file_name) with open(ckpt_file_name, "ab") as f: if enc_key is not None: plain_data = bytes(0) cipher_data = bytes(0) for name, value in data_list.items(): data_size = value[2].nbytes / 1024 if data_size > SLICE_SIZE: slice_count = math.ceil(data_size / SLICE_SIZE) param_slice_list = np.array_split(value[2], slice_count) else: param_slice_list = [value[2]] for param_slice in param_slice_list: checkpoint_list = Checkpoint() param_value = checkpoint_list.value.add() param_value.tag = name param_tensor = param_value.tensor param_tensor.dims.extend(value[0]) param_tensor.tensor_type = value[1] param_tensor.tensor_content = param_slice.tobytes() if enc_key is None: f.write(checkpoint_list.SerializeToString()) else: plain_data += checkpoint_list.SerializeToString() max_block_size = SLICE_SIZE*1024 while len(plain_data) >= max_block_size: cipher_data += _encrypt(plain_data[0: max_block_size], max_block_size, enc_key, len(enc_key), enc_mode) plain_data = plain_data[max_block_size:] if enc_key is not None: if plain_data: cipher_data += _encrypt(plain_data, len(plain_data), enc_key, len(enc_key), enc_mode) f.write(cipher_data) os.chmod(ckpt_file_name, stat.S_IRUSR) except BaseException as e: logger.error("Failed to save the checkpoint file %s.", ckpt_file_name) raise e def save_checkpoint(save_obj, ckpt_file_name, integrated_save=True, async_save=False, append_dict=None, enc_key=None, enc_mode="AES-GCM"): """ Saves checkpoint info to a specified file. Args: save_obj (Union[Cell, list]): The cell object or data list(each element is a dictionary, like [{"name": param_name, "data": param_data},...], the type of param_name would be string, and the type of param_data would be parameter or Tensor). ckpt_file_name (str): Checkpoint file name. If the file name already exists, it will be overwritten. integrated_save (bool): Whether to integrated save in automatic model parallel scene. Default: True async_save (bool): Whether asynchronous execution saves the checkpoint to a file. Default: False append_dict (dict): Additional information that needs to be saved. The key of dict must be str, the value of dict must be one of int float and bool. Default: None enc_key (Union[None, bytes]): Byte type key used for encryption. If the value is None, the encryption is not required. Default: None. enc_mode (str): This parameter is valid only when enc_key is not set to None. Specifies the encryption mode, currently supports 'AES-GCM' and 'AES-CBC'. Default: 'AES-GCM'. Raises: TypeError: If the parameter save_obj is not `nn.Cell` or list type. And if the parameter `integrated_save` and `async_save` are not bool type. Examples: >>> from mindspore import save_checkpoint >>> >>> net = Net() >>> save_checkpoint(net, "lenet.ckpt") """ if not isinstance(save_obj, nn.Cell) and not isinstance(save_obj, list): raise TypeError("The parameter save_obj should be nn.Cell or list, but got {}".format(type(save_obj))) integrated_save = Validator.check_bool(integrated_save) async_save = Validator.check_bool(async_save) append_dict = _check_append_dict(append_dict) enc_key = Validator.check_isinstance('enc_key', enc_key, (type(None), bytes)) enc_mode = Validator.check_isinstance('enc_mode', enc_mode, str) logger.info("Execute the process of saving checkpoint files.") if isinstance(save_obj, nn.Cell): save_obj.init_parameters_data() param_dict = {} for _, param in save_obj.parameters_and_names(): param_dict[param.name] = param param_list = [] for (key, value) in param_dict.items(): each_param = {"name": key} param_data = Tensor(value.data) # in automatic model parallel scenario, some parameters were split to all the devices, # which should be combined before saving if key in save_obj.parameter_layout_dict: param_data = _get_merged_param_data(save_obj, key, param_data, integrated_save) each_param["data"] = param_data param_list.append(each_param) save_obj = param_list if append_dict: append_info_list = [] for k_name, value in append_dict.items(): append_info_list.append({"name": k_name, "data": Tensor(value)}) save_obj.extend(append_info_list) data_list = {} with _ckpt_mutex: for param in save_obj: key = param["name"] ###########--taoht--######################## if 'adam' not in key: data_list[key] = [] if isinstance(param["data"], Parameter): param["data"].init_data() dims = [] if param['data'].shape == (): dims.append(0) else: for dim in param['data'].shape: dims.append(dim) data_list[key].append(dims) tensor_type = str(param["data"].dtype) data_list[key].append(tensor_type) data = param["data"].asnumpy().reshape(-1) data_list[key].append(data) ckpt_file_name = os.path.realpath(ckpt_file_name) if async_save: thr = Thread(target=_exec_save, args=(ckpt_file_name, data_list, enc_key, enc_mode), name="asyn_save_ckpt") thr.start() else: _exec_save(ckpt_file_name, data_list, enc_key, enc_mode) logger.info("Saving checkpoint process is finished.") def _check_param_prefix(filter_prefix, param_name): """Checks whether the prefix of parameter name matches the given filter_prefix.""" for prefix in filter_prefix: if param_name.find(prefix) == 0 \ and (param_name == prefix or param_name[len(prefix)] == "." or (prefix and prefix[-1] == ".")): return True return False def _check_append_dict(append_dict): if append_dict is None: return append_dict if not isinstance(append_dict, dict): raise TypeError(f"The type of append_dict must dict, but got {str(type(append_dict))}.") if not all(isinstance(ele, str) for ele in append_dict.keys()) or \ not all(isinstance(ele, (int, float, bool)) for ele in append_dict.values()): raise TypeError(f"The type of element in append_dict must be key: str, value: int or float.") return append_dict def load(file_name, **kwargs): """ Load MindIR. The returned object can be executed by a `GraphCell`, see class :class:`mindspore.nn.GraphCell` for more details. Args: file_name (str): MindIR file name. kwargs (dict): Configuration options dictionary. - dec_key (bytes): Byte type key used for decryption. Tha valid length is 16, 24, or 32. - dec_mode (str): Specifies the decryption mode, take effect when dec_key is set. Option: 'AES-GCM' | 'AES-CBC'. Default: 'AES-GCM'. Returns: Object, a compiled graph that can executed by `GraphCell`. Raises: ValueError: MindIR file name is incorrect. RuntimeError: Failed to parse MindIR file. Examples: >>> import numpy as np >>> import mindspore.nn as nn >>> from mindspore import Tensor >>> from mindspore.train import export, load >>> >>> net = nn.Conv2d(1, 1, kernel_size=3, weight_init="ones") >>> input = Tensor(np.ones([1, 1, 3, 3]).astype(np.float32)) >>> export(net, input, file_name="net", file_format="MINDIR") >>> graph = load("net.mindir") >>> net = nn.GraphCell(graph) >>> output = net(input) >>> print(output) [[[[4. 6. 4.] [6. 9. 6.] [4. 6. 4.]]]] """ if not isinstance(file_name, str): raise ValueError("The file name must be string.") if not file_name.endswith(".mindir"): raise ValueError("The MindIR should end with mindir, please input the correct file name.") if not os.path.exists(file_name): raise ValueError("The file does not exist.") file_name = os.path.realpath(file_name) logger.info("Execute the process of loading mindir.") if 'dec_key' in kwargs.keys(): dec_key = Validator.check_isinstance('dec_key', kwargs['dec_key'], bytes) dec_mode = 'AES-GCM' if 'dec_mode' in kwargs.keys(): dec_mode = Validator.check_isinstance('dec_mode', kwargs['dec_mode'], str) graph = load_mindir(file_name, dec_key=dec_key, key_len=len(dec_key), dec_mode=dec_mode) else: graph = load_mindir(file_name) if graph is None: if _is_cipher_file(file_name): raise RuntimeError("Load MindIR failed. The file may be encrypted, please pass in the " "correct dec_key and dec_mode.") raise RuntimeError("Load MindIR failed.") return graph def load_checkpoint(ckpt_file_name, net=None, strict_load=False, filter_prefix=None, dec_key=None, dec_mode="AES-GCM"): """ Loads checkpoint info from a specified file. Args: ckpt_file_name (str): Checkpoint file name. net (Cell): Cell network. Default: None strict_load (bool): Whether to strict load the parameter into net. If False, it will load parameter in the param_dict into net with the same suffix and load parameter with different accuracy. Default: False. filter_prefix (Union[str, list[str], tuple[str]]): Parameters starting with the filter_prefix will not be loaded. Default: None. dec_key (Union[None, bytes]): Byte type key used for decryption. If the value is None, the decryption is not required. Default: None. dec_mode (str): This parameter is valid only when dec_key is not set to None. Specifies the decryption mode, currently supports 'AES-GCM' and 'AES-CBC'. Default: 'AES-GCM'. Returns: Dict, key is parameter name, value is a Parameter. Raises: ValueError: Checkpoint file is incorrect. Examples: >>> from mindspore import load_checkpoint >>> >>> ckpt_file_name = "./checkpoint/LeNet5-1_32.ckpt" >>> param_dict = load_checkpoint(ckpt_file_name, filter_prefix="conv1") >>> print(param_dict["conv2.weight"]) Parameter (name=conv2.weight, shape=(16, 6, 5, 5), dtype=Float32, requires_grad=True) """ ckpt_file_name, filter_prefix = _check_checkpoint_param(ckpt_file_name, filter_prefix) dec_key = Validator.check_isinstance('dec_key', dec_key, (type(None), bytes)) dec_mode = Validator.check_isinstance('dec_mode', dec_mode, str) logger.info("Execute the process of loading checkpoint files.") checkpoint_list = Checkpoint() try: if dec_key is None: with open(ckpt_file_name, "rb") as f: pb_content = f.read() else: pb_content = _decrypt(ckpt_file_name, dec_key, len(dec_key), dec_mode) if pb_content is None: raise ValueError checkpoint_list.ParseFromString(pb_content) except BaseException as e: if _is_cipher_file(ckpt_file_name): logger.error("Failed to read the checkpoint file `%s`. The file may be encrypted, please pass in the " "correct dec_key.", ckpt_file_name) else: logger.error("Failed to read the checkpoint file `%s`, please check the correct of the file.", \ ckpt_file_name) raise ValueError(e.__str__()) parameter_dict = {} try: param_data_list = [] for element_id, element in enumerate(checkpoint_list.value): if filter_prefix is not None and _check_param_prefix(filter_prefix, element.tag): continue data = element.tensor.tensor_content data_type = element.tensor.tensor_type np_type = tensor_to_np_type[data_type] ms_type = tensor_to_ms_type[data_type] element_data = np.frombuffer(data, np_type) param_data_list.append(element_data) if (element_id == len(checkpoint_list.value) - 1) or \ (element.tag != checkpoint_list.value[element_id + 1].tag): param_data = np.concatenate((param_data_list), axis=0) param_data_list.clear() dims = element.tensor.dims if dims == [0]: if 'Float' in data_type: param_data = float(param_data[0]) elif 'Int' in data_type: param_data = int(param_data[0]) parameter_dict[element.tag] = Parameter(Tensor(param_data, ms_type), name=element.tag) elif dims == [1]: parameter_dict[element.tag] = Parameter(Tensor(param_data, ms_type), name=element.tag) else: param_dim = [] for dim in dims: param_dim.append(dim) param_value = param_data.reshape(param_dim) parameter_dict[element.tag] = Parameter(Tensor(param_value, ms_type), name=element.tag) logger.info("Loading checkpoint files process is finished.") except BaseException as e: logger.error("Failed to load the checkpoint file `%s`.", ckpt_file_name) raise RuntimeError(e.__str__()) if not parameter_dict: raise ValueError(f"The loaded parameter dict is empty after filtering, please check filter_prefix.") if net is not None: load_param_into_net(net, parameter_dict, strict_load) return parameter_dict def _check_checkpoint_param(ckpt_file_name, filter_prefix=None): """Check function load_checkpoint's parameter.""" if not isinstance(ckpt_file_name, str): raise ValueError("The ckpt_file_name must be string.") if not os.path.exists(ckpt_file_name): raise ValueError("The checkpoint file does not exist.") if ckpt_file_name[-5:] != ".ckpt": raise ValueError("Please input the correct checkpoint file name.") ckpt_file_name = os.path.realpath(ckpt_file_name) if filter_prefix is not None: if not isinstance(filter_prefix, (str, list, tuple)): raise TypeError(f"The type of filter_prefix must be str, list[str] or tuple[str] " f"when filter_prefix is not None, but got {str(type(filter_prefix))}.") if isinstance(filter_prefix, str): filter_prefix = (filter_prefix,) if not filter_prefix: raise ValueError("The filter_prefix can't be empty when filter_prefix is list or tuple.") for index, prefix in enumerate(filter_prefix): if not isinstance(prefix, str): raise TypeError(f"The type of filter_prefix must be str, list[str] or tuple[str], " f"but got {str(type(prefix))} at index {index}.") return ckpt_file_name, filter_prefix def load_param_into_net(net, parameter_dict, strict_load=False): """ Loads parameters into network. Args: net (Cell): Cell network. parameter_dict (dict): Parameter dictionary. strict_load (bool): Whether to strict load the parameter into net. If False, it will load parameter in the param_dict into net with the same suffix and load parameter with different accuracy. Default: False. Returns: List, parameters not loaded in the network. Raises: TypeError: Argument is not a Cell, or parameter_dict is not a Parameter dictionary. Examples: >>> from mindspore import load_checkpoint, load_param_into_net >>> >>> net = Net() >>> ckpt_file_name = "./checkpoint/LeNet5-1_32.ckpt" >>> param_dict = load_checkpoint(ckpt_file_name, filter_prefix="conv1") >>> param_not_load = load_param_into_net(net, param_dict) >>> print(param_not_load) ['conv1.weight'] """ if not isinstance(net, nn.Cell): logger.error("Failed to combine the net and the parameters.") msg = ("Argument net should be a Cell, but got {}.".format(type(net))) raise TypeError(msg) if not isinstance(parameter_dict, dict): logger.error("Failed to combine the net and the parameters.") msg = ("Argument parameter_dict should be a dict, but got {}.".format(type(parameter_dict))) raise TypeError(msg) strict_load = Validator.check_bool(strict_load) logger.info("Execute the process of loading parameters into net.") net.init_parameters_data() param_not_load = [] for _, param in net.parameters_and_names(): if param.name in parameter_dict: new_param = parameter_dict[param.name] if not isinstance(new_param, Parameter): logger.error("Failed to combine the net and the parameters.") msg = ("Argument parameter_dict element should be a Parameter, but got {}.".format(type(new_param))) raise TypeError(msg) _update_param(param, new_param, strict_load) else: param_not_load.append(param.name) if param_not_load and not strict_load: _load_dismatch_prefix_params(net, parameter_dict, param_not_load, strict_load) logger.debug("Params not matched(in net but not in parameter_dict):") for param_name in param_not_load: logger.debug("%s", param_name) logger.info("Loading parameters into net is finished.") if param_not_load: logger.warning("{} parameters in the net are not loaded.".format(len(param_not_load))) for param_name in param_not_load: logger.warning("{} is not loaded.".format(param_name)) return param_not_load def _load_dismatch_prefix_params(net, parameter_dict, param_not_load, strict_load): """When some net parameter did not load, try to continue load.""" prefix_name = "" longest_name = param_not_load[0] while prefix_name != longest_name and param_not_load: logger.debug("Count: {} parameters has not been loaded, try to load continue.".format(len(param_not_load))) prefix_name = longest_name for net_param_name in param_not_load: for dict_name in parameter_dict: if dict_name.endswith(net_param_name): prefix_name = dict_name[:-len(net_param_name)] break if prefix_name != longest_name: break if prefix_name != longest_name: logger.warning("Remove parameter prefix name: {}, continue to load.".format(prefix_name)) for _, param in net.parameters_and_names(): new_param_name = prefix_name + param.name if param.name in param_not_load and new_param_name in parameter_dict: new_param = parameter_dict[new_param_name] _update_param(param, new_param, strict_load) param_not_load.remove(param.name) def _save_graph(network, file_name): """ Saves the graph of network to a file. Args: network (Cell): Obtain a pipeline through network for saving graph. file_name (str): Graph file name into which the graph will be saved. """ logger.info("Execute the process of saving graph.") file_name = os.path.realpath(file_name) graph_pb = network.get_func_graph_proto() if graph_pb: with open(file_name, "wb") as f: os.chmod(file_name, stat.S_IRUSR | stat.S_IWUSR) f.write(graph_pb) def _get_merged_param_data(net, param_name, param_data, integrated_save): """ Gets the merged data(tensor) from tensor slice, by device arrangement and tensor map. Args: net (Cell): MindSpore network. param_name (str): The parameter name, which to be combined. param_data (Tensor): The parameter data on the local device, which was a slice of the whole parameter data. integrated_save (bool): Whether to integrated save in automatic model parallel scene. Returns: Tensor, the combined tensor which with the whole data value. """ from mindspore.parallel._cell_wrapper import get_allgather_cell from mindspore.parallel._tensor import _reshape_param_data layout = net.parameter_layout_dict[param_name] if len(layout) < 6: logger.info("layout dict does not contain the key %s", param_name) return param_data dev_mat = layout[0] tensor_map = layout[1] uniform_split = layout[4] opt_shard_group = layout[5] allgather_net = None mp_weight = False for dim in tensor_map: if dim != -1: mp_weight = True break if param_name in net.parallel_parameter_merge_net_dict: allgather_net = net.parallel_parameter_merge_net_dict[param_name] else: logger.info("need to create allgather net for %s", param_name) if integrated_save: if uniform_split == 0: raise RuntimeError("Integrated save checkpoint only support uniform split tensor now.") # while any dim is not equal to -1, means param is split and needs to be merged # pipeline parallel need to be supported here later if mp_weight: allgather_net = get_allgather_cell(opt_shard_group, bool(opt_shard_group)) elif opt_shard_group: allgather_net = get_allgather_cell(opt_shard_group, False) elif opt_shard_group and context.get_auto_parallel_context("optimizer_weight_shard_aggregated_save"): allgather_net = get_allgather_cell(opt_shard_group, False) net.parallel_parameter_merge_net_dict[param_name] = allgather_net if allgather_net: param_data = allgather_net(param_data) if mp_weight and integrated_save: param_data = _reshape_param_data(param_data, dev_mat, tensor_map) return param_data def _fill_param_into_net(net, parameter_list): """ Fills parameter_list into net. Args: net (Cell): train network. parameter_list (list): parameters list from ge callback. """ parameter_dict = {} for each_param in parameter_list: param_name = each_param["name"] if isinstance(each_param["data"], Parameter): each_param["data"].init_data() np_val = each_param["data"].asnumpy() if np_val.shape == (1,): parameter_dict[param_name] = Parameter(np_val, name=param_name) elif np_val.shape == (): parameter_dict[param_name] = Parameter(Tensor(np_val.tolist(), mstype.pytype_to_dtype(np_val.dtype)), name=param_name) else: parameter_dict[param_name] = Parameter(Tensor(np_val), name=param_name) load_param_into_net(net, parameter_dict) def export(net, *inputs, file_name, file_format='AIR', **kwargs): """ Export the MindSpore prediction model to a file in the specified format. Note: When exporting to AIR、ONNX format, the size of a single tensor can not exceed 2GB. Args: net (Cell): MindSpore network. inputs (Tensor): Inputs of the `net`, if the network has multiple inputs, incoming tuple(Tensor). file_name (str): File name of the model to be exported. file_format (str): MindSpore currently supports 'AIR', 'ONNX' and 'MINDIR' format for exported model. - AIR: Ascend Intermediate Representation. An intermediate representation format of Ascend model. Recommended suffix for output file is '.air'. - ONNX: Open Neural Network eXchange. An open format built to represent machine learning models. Recommended suffix for output file is '.onnx'. - MINDIR: MindSpore Native Intermediate Representation for Anf. An intermediate representation format for MindSpore models. Recommended suffix for output file is '.mindir'. kwargs (dict): Configuration options dictionary. - quant_mode (str): If the network is quantization aware training network, the quant_mode should be set to "QUANT", else the quant_mode should be set to "NONQUANT". - mean (float): The mean of input data after preprocessing, used for quantizing the first layer of network. Default: 127.5. - std_dev (float): The variance of input data after preprocessing, used for quantizing the first layer of network. Default: 127.5. - enc_key (byte): Byte type key used for encryption. Tha valid length is 16, 24, or 32. - enc_mode (str): Specifies the encryption mode, take effect when enc_key is set. Option: 'AES-GCM' | 'AES-CBC'. Default: 'AES-GCM'. Examples: >>> import numpy as np >>> from mindspore import export, Tensor >>> >>> net = LeNet() >>> input = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32)) >>> export(net, Tensor(input), file_name='lenet', file_format='MINDIR') """ logger.info("exporting model file:%s format:%s.", file_name, file_format) check_input_data(*inputs, data_class=Tensor) if not isinstance(file_name, str): raise ValueError("Args file_name {} must be string, please check it".format(file_name)) Validator.check_file_name_by_regular(file_name) file_name = os.path.realpath(file_name) net = _quant_export(net, *inputs, file_format=file_format, **kwargs) if 'enc_key' in kwargs.keys(): if file_format != 'MINDIR': raise ValueError(f"enc_key can be passed in only when file_format=='MINDIR', but got {file_format}") enc_key = Validator.check_isinstance('enc_key', kwargs['enc_key'], bytes) enc_mode = 'AES-GCM' if 'enc_mode' in kwargs.keys(): enc_mode = Validator.check_isinstance('enc_mode', kwargs['enc_mode'], str) _export(net, file_name, file_format, *inputs, enc_key=enc_key, enc_mode=enc_mode) else: _export(net, file_name, file_format, *inputs) def _export(net, file_name, file_format, *inputs, **kwargs): """ It is an internal conversion function. Export the MindSpore prediction model to a file in the specified format. """ logger.info("exporting model file:%s format:%s.", file_name, file_format) check_input_data(*inputs, data_class=Tensor) if file_format == 'GEIR': logger.warning(f"Format 'GEIR' is deprecated, it would be removed in future release, use 'AIR' instead.") file_format = 'AIR' supported_formats = ['AIR', 'ONNX', 'MINDIR'] if file_format not in supported_formats: raise ValueError(f'Illegal file format {file_format}, it must be one of {supported_formats}') # When dumping ONNX file, switch network mode to infer when it is training(NOTE: ONNX only designed for prediction) is_dump_onnx_in_training = net.training and file_format == 'ONNX' if is_dump_onnx_in_training: net.set_train(mode=False) if file_format == 'AIR': phase_name = 'export.air' graph_id, _ = _executor.compile(net, *inputs, phase=phase_name) if not file_name.endswith('.air'): file_name += ".air" if os.path.exists(file_name): os.chmod(file_name, stat.S_IWUSR) if "/" in file_name: real_path = os.path.realpath(file_name[:file_name.rfind("/")]) os.makedirs(real_path, exist_ok=True) _executor.export(file_name, graph_id) os.chmod(file_name, stat.S_IRUSR) elif file_format == 'ONNX': total_size = _calculation_net_size(net) if total_size > PROTO_LIMIT_SIZE: raise RuntimeError('Export onnx model failed. Network size is: {}G, it exceeded the protobuf: {}G limit.' .format(total_size/1024/1024, PROTO_LIMIT_SIZE/1024/1024)) phase_name = 'export.onnx' graph_id, _ = _executor.compile(net, *inputs, phase=phase_name, do_convert=False) onnx_stream = _executor._get_func_graph_proto(net, graph_id) if not file_name.endswith('.onnx'): file_name += ".onnx" if os.path.exists(file_name): os.chmod(file_name, stat.S_IWUSR) with open(file_name, 'wb') as f: f.write(onnx_stream) os.chmod(file_name, stat.S_IRUSR) elif file_format == 'MINDIR': _save_mindir(net, file_name, *inputs, **kwargs) if is_dump_onnx_in_training: net.set_train(mode=True) def _save_mindir(net, file_name, *inputs, **kwargs): """Save MindIR format file.""" model = mindir_model() phase_name = "predict" if net._auto_parallel_mode else "export.mindir" graph_id, _ = _executor.compile(net, *inputs, phase=phase_name, do_convert=False, auto_parallel_mode=net._auto_parallel_mode) mindir_stream = _executor._get_func_graph_proto(net, graph_id, 'mind_ir') net_dict = net.parameters_dict() model.ParseFromString(mindir_stream) save_together = _save_together(net_dict, model) is_encrypt = lambda: 'enc_key' in kwargs.keys() and 'enc_mode' in kwargs.keys() if save_together: _save_mindir_together(net_dict, model, file_name, is_encrypt, **kwargs) else: logger.warning("Parameters in the net capacity exceeds 1G, save MindIR model and parameters separately.") # save parameter file_prefix = file_name.split("/")[-1] if file_prefix.endswith(".mindir"): file_prefix = file_prefix[:-7] current_path = os.path.abspath(file_name) dirname = os.path.dirname(current_path) data_path = os.path.join(dirname, file_prefix + "_variables") if os.path.exists(data_path): shutil.rmtree(data_path) os.makedirs(data_path, exist_ok=True) index = 0 graphproto = graph_proto() data_size = 0 for name, param in net_dict.items(): for param_proto in model.graph.parameter: if name == param_proto.name[param_proto.name.find(":") + 1:]: parameter = graphproto.parameter.add() parameter.name = param_proto.name parameter.data_type = param_proto.data_type for dim in param_proto.dims: parameter.dims.append(dim) byte_data = param.data.asnumpy().tobytes() parameter.raw_data = byte_data data_size += sys.getsizeof(byte_data) / 1024 break if data_size > TOTAL_SAVE: data_file_name = os.path.join(data_path, "data_" + str(index)) if os.path.exists(data_file_name): os.chmod(data_file_name, stat.S_IWUSR) with open(data_file_name, "ab") as f: os.chmod(data_file_name, stat.S_IRUSR | stat.S_IWUSR) graph_string = graphproto.SerializeToString() if is_encrypt(): graph_string = _encrypt(graph_string, len(graph_string), kwargs['enc_key'], len(kwargs['enc_key']), kwargs['enc_mode']) f.write(graph_string) os.chmod(data_file_name, stat.S_IRUSR) index += 1 data_size = 0 del graphproto.parameter[:] if graphproto.parameter: data_file_name = os.path.join(data_path, "data_" + str(index)) if os.path.exists(data_file_name): os.chmod(data_file_name, stat.S_IWUSR) with open(data_file_name, "ab") as f: os.chmod(data_file_name, stat.S_IRUSR | stat.S_IWUSR) graph_string = graphproto.SerializeToString() if is_encrypt(): graph_string = _encrypt(graph_string, len(graph_string), kwargs['enc_key'], len(kwargs['enc_key']), kwargs['enc_mode']) f.write(graph_string) os.chmod(data_file_name, stat.S_IRUSR) # save graph del model.graph.parameter[:] graph_file_name = os.path.join(dirname, file_prefix + "_graph.mindir") if os.path.exists(graph_file_name): os.chmod(graph_file_name, stat.S_IWUSR) with open(graph_file_name, 'wb') as f: os.chmod(graph_file_name, stat.S_IRUSR | stat.S_IWUSR) model_string = model.SerializeToString() if is_encrypt(): model_string = _encrypt(model_string, len(model_string), kwargs['enc_key'], len(kwargs['enc_key']), kwargs['enc_mode']) f.write(model_string) os.chmod(graph_file_name, stat.S_IRUSR) def _save_mindir_together(net_dict, model, file_name, is_encrypt, **kwargs): """Save graph and parameter together.""" for param_proto in model.graph.parameter: param_name = param_proto.name[param_proto.name.find(":") + 1:] if param_name in net_dict.keys(): param_data = net_dict[param_name].data.asnumpy().tobytes() param_proto.raw_data = param_data else: logger.error("The parameter %s in the graph are not in the network.", param_name) raise ValueError("The parameter in the graph must in the network.") if not file_name.endswith('.mindir'): file_name += ".mindir" current_path = os.path.abspath(file_name) dirname = os.path.dirname(current_path) os.makedirs(dirname, exist_ok=True) if os.path.exists(file_name): os.chmod(file_name, stat.S_IWUSR) with open(file_name, 'wb') as f: os.chmod(file_name, stat.S_IRUSR | stat.S_IWUSR) model_string = model.SerializeToString() if is_encrypt(): model_string = _encrypt(model_string, len(model_string), kwargs['enc_key'], len(kwargs['enc_key']), kwargs['enc_mode']) f.write(model_string) os.chmod(file_name, stat.S_IRUSR) def _save_together(net_dict, model): """Whether graph and parameter save together during save mindir model.""" data_total = 0 for param_proto in model.graph.parameter: name = param_proto.name[param_proto.name.find(":") + 1:] if name in net_dict.keys(): data_total += sys.getsizeof(net_dict[name].data.asnumpy().tobytes()) / 1024 else: raise RuntimeError('Graph parameter: {} Undefined in network.'.format(param_proto.name)) if data_total > TOTAL_SAVE: return False return True def quant_mode_manage(func): """ Inherit the quant_mode in old version. """ def warpper(network, *inputs, file_format, **kwargs): if 'quant_mode' not in kwargs: return network quant_mode = kwargs['quant_mode'] if not isinstance(quant_mode, str): raise TypeError("The type of quant_mode should be str, but got {}.".format(type(quant_mode))) if quant_mode in ('AUTO', 'MANUAL'): kwargs['quant_mode'] = 'QUANT' return func(network, *inputs, file_format=file_format, **kwargs) return warpper @quant_mode_manage def _quant_export(network, *inputs, file_format, **kwargs): """ Exports MindSpore quantization predict model to deploy with AIR and MINDIR. """ supported_device = ["Ascend", "GPU"] supported_formats = ['AIR', 'MINDIR'] quant_mode_formats = ['QUANT', 'NONQUANT'] quant_mode = kwargs['quant_mode'] if quant_mode not in quant_mode_formats: raise KeyError(f'Quant_mode input is wrong, Please choose the right mode of the quant_mode.') if quant_mode == 'NONQUANT': return network quant_net = copy.deepcopy(network) quant_net._create_time = int(time.time() * 1e9) mean = 127.5 if kwargs.get('mean', None) is None else kwargs['mean'] std_dev = 127.5 if kwargs.get('std_dev', None) is None else kwargs['std_dev'] mean = Validator.check_value_type("mean", mean, (int, float)) std_dev = Validator.check_value_type("std_dev", std_dev, (int, float)) if context.get_context('device_target') not in supported_device: raise KeyError("Unsupported {} device target.".format(context.get_context('device_target'))) if file_format not in supported_formats: raise ValueError('Illegal file format {}.'.format(file_format)) quant_net.set_train(False) if file_format == "MINDIR": exporter = quant_export.ExportToQuantInferNetwork(quant_net, mean, std_dev, *inputs, is_mindir=True) else: exporter = quant_export.ExportToQuantInferNetwork(quant_net, mean, std_dev, *inputs) deploy_net = exporter.run() return deploy_net def parse_print(print_file_name): """ Loads Print data from a specified file. Args: print_file_name (str): The file name of saved print data. Returns: List, element of list is Tensor. Raises: ValueError: The print file may be empty, please make sure enter the correct file name. """ print_file_path = os.path.realpath(print_file_name) if os.path.getsize(print_file_path) == 0: raise ValueError("The print file may be empty, please make sure enter the correct file name.") logger.info("Execute load print process.") print_list = Print() try: with open(print_file_path, "rb") as f: pb_content = f.read() print_list.ParseFromString(pb_content) except BaseException as e: logger.error("Failed to read the print file %s, please check the correct of the file.", print_file_name) raise ValueError(e.__str__()) tensor_list = [] try: for print_ in print_list.value: # String type if print_.HasField("desc"): tensor_list.append(print_.desc) elif print_.HasField("tensor"): dims = print_.tensor.dims data_type = print_.tensor.tensor_type data = print_.tensor.tensor_content np_type = tensor_to_np_type[data_type] param_data = np.fromstring(data, np_type) ms_type = tensor_to_ms_type[data_type] if dims and dims != [0]: param_value = param_data.reshape(dims) tensor_list.append(Tensor(param_value, ms_type)) # Scalar type else: data_type_ = data_type.lower() if 'float' in data_type_: param_data = float(param_data[0]) elif 'int' in data_type_: param_data = int(param_data[0]) elif 'bool' in data_type_: param_data = bool(param_data[0]) tensor_list.append(Tensor(param_data, ms_type)) except BaseException as e: logger.error("Failed to load the print file %s.", print_list) raise RuntimeError(e.__str__()) return tensor_list def _merge_param_with_strategy(sliced_data, parameter_name, strategy, is_even): """ Merge data slices to one tensor with whole data when strategy is not None. Args: sliced_data (list[numpy.ndarray]): Data slices in order of rank_id. parameter_name (str): Name of parameter. strategy (dict): Parameter slice strategy. is_even (bool): Slice manner that True represents slicing evenly and False represents slicing unevenly. Returns: Tensor, the merged Tensor which has the whole data. Raises: ValueError: Failed to merge. """ layout = strategy.get(parameter_name) try: dev_mat = list(layout.dev_matrix[0].dim) tensor_map = list(layout.tensor_map[0].dim) param_split_shape = list(layout.param_split_shape[0].dim) field_size = int(layout.field) except BaseException as e: raise ValueError(f"{e.__str__()}. Please make sure that strategy matches the node_strategy.proto.") device_count = 1 for dim in dev_mat: device_count *= dim if len(sliced_data) != device_count: raise ValueError(f"The sliced_parameters length should be equal to device_count. " f"the sliced_parameters length is {len(sliced_data)} but device_count is {device_count}.") if not param_split_shape: if not is_even: raise ValueError("The shape of every parameter in sliced_parameters should be the same " "when slice manner is even.") all_gather_tensor = Tensor(np.concatenate(sliced_data)) if field_size > 0: from mindspore.parallel._tensor import _reshape_param_data_with_weight merged_tensor = _reshape_param_data_with_weight(all_gather_tensor, dev_mat, field_size) else: from mindspore.parallel._tensor import _reshape_param_data merged_tensor = _reshape_param_data(all_gather_tensor, dev_mat, tensor_map) else: tensor_strategy = _get_tensor_strategy(dev_mat, tensor_map) slice_count = 1 for dim in tensor_strategy: slice_count *= dim if len(param_split_shape) != slice_count: raise ValueError(f"The param_split_shape length in strategy should be {slice_count}, " f"but got {len(param_split_shape)}.") tensor_slices_new = list(range(slice_count)) tensor_slices = sliced_data for i in range(device_count): slice_index = int(_get_tensor_slice_index(dev_mat, tensor_strategy, tensor_map, i)) if tensor_slices[i].shape[0] != param_split_shape[slice_index]: raise ValueError(f"The slice {slice_index} is {param_split_shape[slice_index]} in 0 axis, " f"but got {tensor_slices[i].shape[0]}.") tensor_slices_new[slice_index] = np.array(tensor_slices[i]) dim_len = len(tensor_strategy) for i in range(dim_len): ele_count = int(len(tensor_slices_new) / tensor_strategy[dim_len - 1 - i]) tensor_slices_new_inner = [] for j in range(ele_count): new_tensor = tensor_slices_new[j * tensor_strategy[dim_len - 1 - i]] for l in range(j * tensor_strategy[dim_len - 1 - i] + 1, (j + 1) * tensor_strategy[dim_len - 1 - i]): new_tensor = np.concatenate((new_tensor, tensor_slices_new[l]), axis=dim_len - 1 - i) tensor_slices_new_inner.insert(len(tensor_slices_new_inner), np.array(new_tensor)) tensor_slices_new = tensor_slices_new_inner merged_tensor = Tensor(tensor_slices_new[0]) return merged_tensor def build_searched_strategy(strategy_filename): """ Build strategy of every parameter in network. Args: strategy_filename (str): Name of strategy file. Returns: Dict, whose key is parameter name and value is slice strategy of this parameter. Raises: ValueError: Strategy file is incorrect. TypeError: strategy_filename is not str. """ if not isinstance(strategy_filename, str): raise TypeError(f"The strategy_filename should be str, but got {type(strategy_filename)}.") if not os.path.isfile(strategy_filename): raise ValueError(f"No such strategy file: {strategy_filename}.") if os.path.getsize(strategy_filename) == 0: raise ValueError("The strategy file should not be empty.") parallel_strategy_map = ParallelStrategyMap() with open(strategy_filename, 'rb') as f: pb_content = f.read() parallel_strategy_map.ParseFromString(pb_content) layout_items = parallel_strategy_map.parallel_layout_item if not layout_items: raise ValueError("The strategy file has no sliced parameter.") strategy = {} for layout_item in layout_items: parameter_name = layout_item.param_name layout = layout_item.parallel_layouts strategy[parameter_name] = layout return strategy def merge_sliced_parameter(sliced_parameters, strategy=None): """ Merge parameter slices to one whole parameter. Args: sliced_parameters (list[Parameter]): Parameter slices in order of rank_id. strategy (Optional[dict]): Parameter slice strategy, whose key is parameter name and value is slice strategy of this parameter. If strategy is None, just merge parameter slices in 0 axis order. Default: None. Returns: Parameter, the merged parameter which has the whole data. Raises: ValueError: Failed to merge. TypeError: The sliced_parameters is incorrect or strategy is not dict. KeyError: The parameter name is not in keys of strategy. Examples: >>> import numpy as np >>> from mindspore import Tensor >>> from mindspore.common.parameter import Parameter >>> from mindspore.train import merge_sliced_parameter >>> >>> sliced_parameters = [ ... Parameter(Tensor(np.array([0.00023915, 0.00013939, -0.00098059])), ... "network.embedding_table"), ... Parameter(Tensor(np.array([0.00015815, 0.00015458, -0.00012125])), ... "network.embedding_table"), ... Parameter(Tensor(np.array([0.00042165, 0.00029692, -0.00007941])), ... "network.embedding_table"), ... Parameter(Tensor(np.array([0.00084451, 0.00089960, -0.00010431])), ... "network.embedding_table")] >>> merged_parameter = merge_sliced_parameter(sliced_parameters) >>> print(merged_parameter) Parameter (name=network.embedding_table, shape=(12,), dtype=Float64, requires_grad=True) """ if not isinstance(sliced_parameters, list): raise TypeError(f"The sliced_parameters should be list, but got {type(sliced_parameters)}.") if not sliced_parameters: raise ValueError("The sliced_parameters should not be empty.") if strategy and not isinstance(strategy, dict): raise TypeError(f"The strategy should be dict, but got {type(strategy)}.") try: parameter_name = sliced_parameters[0].name parameter_shape = sliced_parameters[0].data.shape parameter_shape_length = len(parameter_shape) except BaseException as e: raise TypeError(f"{e.__str__()}. the element in sliced_parameters should be Parameter.") is_even = True for index, parameter in enumerate(sliced_parameters): if not isinstance(parameter, Parameter): raise TypeError(f"The element in sliced_parameters should be Parameter, " f"but got {type(parameter)} at index {index}.") if parameter.name != parameter_name \ or len(parameter.data.shape) != parameter_shape_length \ or parameter.data.shape[1:] != parameter_shape[1:]: raise ValueError("Please make sure that the elements in slice_parameters have the same name, " "dimension length and shape except 0 axis") if parameter.data.shape != parameter_shape: is_even = False layerwise_parallel = sliced_parameters[0].layerwise_parallel requires_grad = sliced_parameters[0].requires_grad sliced_data = [parameter.data.asnumpy() for parameter in sliced_parameters] if not strategy: merged_tensor = Tensor(np.concatenate(sliced_data)) merged_parameter = Parameter(merged_tensor, parameter_name, requires_grad, layerwise_parallel) else: if parameter_name not in strategy.keys(): raise KeyError(f"The parameter name should be one key of strategy. " f"the parameter name is {parameter_name}.") merged_tensor = _merge_param_with_strategy(sliced_data, parameter_name, strategy, is_even) merged_parameter = Parameter(merged_tensor, parameter_name, requires_grad, layerwise_parallel) return merged_parameter def load_distributed_checkpoint(network, checkpoint_filenames, predict_strategy=None, train_strategy_filename=None, dec_key=None, dec_mode='AES-GCM'): """ Load checkpoint into net for distributed predication. Args: network (Cell): Network for distributed predication. checkpoint_filenames (list[str]): The name of Checkpoint files in order of rank id. predict_strategy (dict): Strategy of predication process, whose key is parameter name, and value is a list or a tuple that the first four elements are [dev_matrix, tensor_map, param_split_shape, field]. If None, it means that the predication process just uses single device. Default: None. train_strategy_filename (str): Train strategy proto file name. Default: None. dec_key (Union[None, bytes]): Byte type key used for decryption. If the value is None, the decryption is not required. Default: None. dec_mode (str): This parameter is valid only when dec_key is not set to None. Specifies the decryption mode, currently supports 'AES-GCM' and 'AES-CBC'. Default: 'AES-GCM'. Raises: TypeError: The type of inputs do not match the requirements. ValueError: Failed to load checkpoint into net. """ network = Validator.check_isinstance("network", network, nn.Cell) _check_checkpoint_file(checkpoint_filenames) _check_predict_strategy(predict_strategy) dec_key = Validator.check_isinstance('dec_key', dec_key, (type(None), bytes)) dec_mode = Validator.check_isinstance('dec_mode', dec_mode, str) if train_strategy_filename is None: train_strategy_filename = context.get_auto_parallel_context("strategy_ckpt_load_file") _train_strategy = build_searched_strategy(train_strategy_filename) train_strategy = _convert_to_list(_train_strategy) train_dev_count = 1 ckpt_file_len = len(checkpoint_filenames) for dim in train_strategy[list(train_strategy.keys())[0]][0]: train_dev_count *= dim if train_dev_count != ckpt_file_len: raise ValueError( f"The length of checkpoint_filenames should be equal to the device count of training process. " f"The length is {ckpt_file_len} but the device count is {train_dev_count}.") rank_list = _infer_rank_list(train_strategy, predict_strategy) param_total_dict = defaultdict(dict) for file_index, file_name in enumerate(checkpoint_filenames): ckpt_dict = load_checkpoint(file_name, dec_key=dec_key, dec_mode=dec_mode) for param_name, param in ckpt_dict.items(): param_total_dict[param_name][file_index] = param param_dict = {} param_not_in_strategy = [] param_not_in_ckpt = [] for _, param in network.parameters_and_names(): sliced_params = [] if param.name not in rank_list.keys(): param_not_in_strategy.append(param.name) continue if param.name not in param_total_dict: param_not_in_ckpt.append(param.name) continue param_rank = rank_list[param.name][0] skip_merge_split = rank_list[param.name][1] shard_stride = train_strategy[param.name][4] if train_strategy[param.name][5]: ##----taoht------ shard_size = 64 #ckpt_file_len // shard_stride // train_strategy[param.name][5] # shard_size = ckpt_file_len // shard_stride // train_strategy[param.name][5] else: shard_size = 0 for rank in param_rank: param_total_list = list(range(0, ckpt_file_len)) if shard_size > 0: shard_total_list = [param_total_list[i:i + shard_size] for i in range(0, ckpt_file_len, shard_size)] param_total_list = shard_total_list[rank // shard_size] if shard_stride > 0: param_stride = [] # merge pre parameter param_index = param_total_list[0:param_total_list.index(rank) + 1][::-1][::shard_stride] param_index.extend(param_total_list[param_total_list.index(rank):][::shard_stride]) param_index = list(set(param_index)) param_index.sort() # print("000000000000000000"*10) # print("param.name: ", param.name) # print("shard_size: ", shard_size) # print("shard_stride", shard_stride) # print("param_index: ", param_index) for rank_num in param_index: param_stride.append(param_total_dict[param.name][rank_num].data.asnumpy()) sliced_param = Parameter(Tensor(np.concatenate(param_stride)), name=param.name) else: sliced_param = param_total_dict[param.name][rank] # print("11111111111111111"*10) # print('sliced_param', sliced_param) sliced_params.append(sliced_param) if skip_merge_split: split_param = sliced_params[0] else: param_unique_strategy = _remove_repeated_slices(train_strategy[param.name]) _param_unique_strategy = _convert_to_layout(param.name, param_unique_strategy) split_param = _merge_and_split(sliced_params, _param_unique_strategy, predict_strategy) opt_shard_group = predict_strategy[param.name][5] if predict_strategy else None if opt_shard_group: data = split_param.data.asnumpy() rank = get_rank(opt_shard_group) size = get_group_size(opt_shard_group) try: data_slice = np.split(data, size)[rank] except BaseException as e: logger.error("Failed to load opt shard slice in load distributed checkpoint for {}. Data shape is {}" " and group is {}".format(param.name, split_param.data.shape, opt_shard_group)) raise RuntimeError(e.__str__()) split_param = Parameter(Tensor(data_slice), param.name, split_param.requires_grad, split_param.layerwise_parallel) param_dict[param.name] = split_param if param_not_in_strategy: logger.warning("{} parameters in network are not in the slice strategy.".format(param_not_in_strategy)) if param_not_in_ckpt: logger.warning("{} parameters in slice strategy but not in the checkpoint file.".format(param_not_in_ckpt)) load_param_into_net(network, param_dict) def async_ckpt_thread_status(): """ Get async save checkpoint thread status. Returns: True, Asynchronous save checkpoint thread is running. False, Asynchronous save checkpoint thread is not executing. """ thr_list = threading.enumerate() return True in [ele.getName() == "asyn_save_ckpt" for ele in thr_list] def _check_predict_strategy(predict_strategy): """Check predict strategy.""" def _check_int_list(arg): if not isinstance(arg, list): return False for item in arg: if not isinstance(item, int): return False return True if predict_strategy is None: return flag = True predict_strategy = Validator.check_isinstance("predict_strategy", predict_strategy, dict) for key in predict_strategy.keys(): if not isinstance(key, str) or not isinstance(predict_strategy[key], (list, tuple)) \ or len(predict_strategy[key]) < 4: flag = False dev_matrix, tensor_map, param_split_shape, field_size = predict_strategy[key][:4] if not _check_int_list(dev_matrix) or not _check_int_list(tensor_map) or \ not (_check_int_list(param_split_shape) or not param_split_shape) or \ not (isinstance(field_size, int) and field_size == 0): flag = False if not flag: raise ValueError(f"Please make sure that the key of predict_strategy is str, " f"and the value is a list or a tuple that the first four elements are " f"dev_matrix (list[int]), tensor_map (list[int]), " f"param_split_shape (list[int]) and field_size (zero).") def _check_checkpoint_file(checkpoint_filenames): """Check checkpoint file name.""" for index, filename in enumerate(checkpoint_filenames): if not isinstance(filename, str) or not os.path.exists(filename) \ or filename[-5:] != ".ckpt" or os.path.getsize(filename) == 0: raise ValueError(f"Please make sure that the {filename} at index {index} is a valid checkpoint file.") def _convert_to_list(strategy): """Convert ParallelLayouts object to specified list.""" train_map = {} for param_name in strategy.keys(): try: layout = strategy.get(param_name) dev_mat = list(layout.dev_matrix[0].dim) tensor_map = list(layout.tensor_map[0].dim) param_split_shape = list(layout.param_split_shape[0].dim) field_size = int(layout.field) shard_stride = int(layout.opt_weight_shard_step) shard_size = int(layout.opt_weight_shard_size) train_map[param_name] = [dev_mat, tensor_map, param_split_shape, field_size, shard_stride, shard_size] except BaseException as e: raise ValueError(f"{e.__str__()}. Please make sure that strategy matches the node_strategy.proto.") return train_map def _convert_to_layout(param_name, tensor_layout): """Convert list to ParallelLayouts object.""" strategy = {} try: layout = ParallelLayouts() layout.field = tensor_layout[3] dev_matrix = layout.dev_matrix.add() for item in tensor_layout[0]: dev_matrix.dim.append(item) tensor_map = layout.tensor_map.add() for item in tensor_layout[1]: tensor_map.dim.append(item) param_split_shape = layout.param_split_shape.add() for item in tensor_layout[2]: param_split_shape.dim.append(item) except BaseException as e: raise ValueError("Convert failed. " + e.__str__()) strategy[param_name] = layout return strategy def _merge_and_split(sliced_params, train_strategy, predict_strategy): """Merge sliced parameter and split it according to the predict strategy.""" merged_param = merge_sliced_parameter(sliced_params, train_strategy) if predict_strategy is None: return merged_param param_name = merged_param.name tensor_layout = predict_strategy[param_name] split_tensor = _load_tensor(merged_param.data, tensor_layout[0], tensor_layout[1]) requires_grad = merged_param.requires_grad layerwise_parallel = merged_param.layerwise_parallel split_param = Parameter(split_tensor, param_name, requires_grad, layerwise_parallel) return split_param def _calculation_net_size(net): """Calculate the size of parameters in the network.""" data_total = 0 net_dict = net.parameters_dict() for name in net_dict: data_total += sys.getsizeof(net_dict[name].data.asnumpy().tobytes()) / 1024 return data_total pcl_pangu/pcl_pangu/model/panguAlpha_mindspore/src/tokenization_jieba.py000066400000000000000000000045751443434453700272750ustar00rootroot00000000000000# coding=utf-8 # Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization classes for OpenAI GPT.""" from __future__ import (absolute_import, division, print_function, unicode_literals) from io import open import sentencepiece as spm import jieba class JIEBATokenizer(): r""" Jieba Tokenizer """ def __init__(self, vocab_file, model_file, max_len=None): self.max_len = max_len if max_len is not None else int(1e12) f = open(vocab_file, 'r') lines = f.readlines() self.encoder = {} for line in enumerate(lines): key = line[1].split('\t')[0] self.encoder[key] = line[0] self.decoder = {v: k for k, v in self.encoder.items()} self.sp = spm.SentencePieceProcessor(model_file=model_file) self.translator = str.maketrans(" \n", "\u2582\u2583") self.eod_id = self.encoder[''] self.eot_id = self.encoder[''] self.pad_id = self.encoder[''] @property def vocab_size(self): return len(self.encoder) def __len__(self): return len(self.encoder) + len(self.special_tokens) @property def eod(self): return self.eod_id def tokenize(self, text): """ Tokenize a string. """ seg_list = [x.translate(self.translator) for x in jieba.cut(text, cut_all=False)] new_seg = " ".join(seg_list) return self.sp.encode(new_seg) def convert_tokens_to_ids(self, tokens): return tokens def convert_ids_to_tokens(self, ids): return self.decode(ids) def encode(self, text): res = self.tokenize(text) return res def decode(self, tokens): text = self.sp.decode(tokens) text = text.replace(' ', '').replace('\u2582', ' ').replace('\u2583', '\n') return text pcl_pangu/pcl_pangu/model/panguAlpha_mindspore/src/utils_pangu.py000066400000000000000000001054731443434453700257560ustar00rootroot00000000000000# Copyright 2022 PCL, Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """ network config setting, gradient clip function and dynamic learning rate function """ import argparse import ast import os import time import hashlib import numpy as np import mindspore.nn as nn from mindspore import context from mindspore.ops import operations as P from mindspore.ops import composite as C from mindspore.ops import functional as F import mindspore.common.dtype as mstype from mindspore.common.tensor import Tensor from mindspore.nn.learning_rate_schedule import LearningRateSchedule, PolynomialDecayLR, WarmUpLR, CosineDecayLR from mindspore.parallel._auto_parallel_context import auto_parallel_context from mindspore.communication.management import get_rank, get_group_size, create_group from mindspore.common import Parameter, ParameterTuple from mindspore.common.initializer import initializer from mindspore.nn.optim.optimizer import Optimizer from mindspore.nn.optim.adam import _adam_opt, _check_param_value from mindspore.train.callback import Callback from mindspore.train.summary import SummaryRecord from multiprocessing import Process try: import moxing as mox modelarts_flag = True except: modelarts_flag = False def WorkEnvironment(environment): if environment == 'train': workroot = '/home/work/user-job-dir' elif environment == 'debug': workroot = '/home/ma-user/work' print('current work mode:' + environment + ', workroot:' + workroot) return workroot class AdamWeightDecay(Optimizer): def __init__(self, params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-6, weight_decay=0.0): super(AdamWeightDecay, self).__init__(learning_rate, params, weight_decay) _check_param_value(beta1, beta2, eps, self.cls_name) self.beta1 = Tensor(np.array([beta1]).astype(np.float32)) self.beta2 = Tensor(np.array([beta2]).astype(np.float32)) self.eps = Tensor(np.array([eps]).astype(np.float32)) self.moments1 = self.parameters.clone(prefix="adam_m", init='zeros') self.moments2 = self.parameters.clone(prefix="adam_v", init='zeros') self.hyper_map = C.HyperMap() def construct(self, gradients): lr = self.get_lr() if self.is_group: if self.is_group_lr: optim_result = self.hyper_map(F.partial(_adam_opt, self.beta1, self.beta2, self.eps), lr, self.weight_decay, self.parameters, self.moments1, self.moments2, gradients, self.decay_flags, self.optim_filter) else: optim_result = self.hyper_map(F.partial(_adam_opt, self.beta1, self.beta2, self.eps, lr), self.weight_decay, self.parameters, self.moments1, self.moments2, gradients, self.decay_flags, self.optim_filter) else: optim_result = self.hyper_map(F.partial(_adam_opt, self.beta1, self.beta2, self.eps, lr, self.weight_decay), self.parameters, self.moments1, self.moments2, gradients, self.decay_flags, self.optim_filter) if self.use_parallel: self.broadcast_params(optim_result) return optim_result, lr class FP32StateAdamWeightDecay(AdamWeightDecay): r""" This class is almost same with the mindspore's AdamWeightDecay implements, the only difference is the optimizer's state will be always initialized with float32, where the original AdamWeightDecay will initialize the optimizer's state with float16, if the parameters are initialized with fp16. This setting will avoid overflow in training PanGu-Alpha model using fp16. """ def __init__(self, params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-6, weight_decay=0.0): super(FP32StateAdamWeightDecay, self).__init__(params, learning_rate=learning_rate, beta1=beta1, beta2=beta2, eps=eps, weight_decay=weight_decay) self.moments1 = self.clone_state(self.parameters, prefix='adam_m', init='zeros') self.moments2 = self.clone_state(self.parameters, prefix='adam_v', init='zeros') def clone_state(self, parameter_tuple, prefix, init): r""" parameter_tuple: ParameterTuple. The parameters of the network prefix: str. The prefix name of the parameters init: str. The initialization method """ new = [] for old_param in parameter_tuple: new_state = Parameter(initializer(init, shape=old_param.shape, dtype=mstype.float32)) new_state.param_info = old_param.param_info.clone() new_state.is_init = False new_state.set_data(initializer(init, shape=old_param.shape, dtype=mstype.float32)) new_state.name = prefix + '.' + new_state.name new.append(new_state) return ParameterTuple(new) get_square_sum = C.MultitypeFuncGraph("get_square_sum") @get_square_sum.register("Tensor", "Number") def _get_square_sum(grad, value): norm = P.ReduceSum(False)(F.square(grad), ()) / value norm = F.expand_dims(F.cast(norm, mstype.float32), 0) return norm apply_global_norm = C.MultitypeFuncGraph("apply_global_norm") @apply_global_norm.register("Tensor", "Tensor", "Tensor") def _apply_global_norm(clip_norm, global_norm, grad): grad = grad * clip_norm / global_norm return grad def _get_model_parallel_group(mp): """ Calculate the communication group of model parallel dim in one pipeline stage """ rank = get_rank() stage_nums = auto_parallel_context().get_pipeline_stages() device_nums = get_group_size() per_stage_device_nums = device_nums // stage_nums stage_id = rank // per_stage_device_nums local_stage_rank_id = rank % per_stage_device_nums index = local_stage_rank_id // mp group = range(0, mp) rank_str_list = [str(x + index * mp + stage_id * per_stage_device_nums) for x in group] rank_list_str = "-".join(rank_str_list) rank_list = [x + index * mp + stage_id * per_stage_device_nums for x in group] return rank_list, rank_list_str def _get_pipeline_group(): """ Calculate the communication group between all pipeline stages """ rank = get_rank() stage_nums = auto_parallel_context().get_pipeline_stages() device_nums = get_group_size() per_stage_device_nums = device_nums // stage_nums local_stage_rank_id = rank % per_stage_device_nums group = range(0, stage_nums) rank_list = [local_stage_rank_id + x * per_stage_device_nums for x in group] rank_str_list = [str(local_stage_rank_id + x * per_stage_device_nums) for x in group] rank_list_str = "-".join(rank_str_list) return rank_list, rank_list_str class GlobalNorm(nn.Cell): """ Calculate the global norm value of given tensors """ def __init__(self, params, config): super(GlobalNorm, self).__init__() self.norm = nn.Norm() self.hyper_map = C.HyperMap() self.is_pipeline = (config.stage_num > 1) if self.is_pipeline: if context.get_auto_parallel_context("enable_parallel_optimizer"): group_size = get_group_size() // config.stage_num else: group_size = config.mp group_list, group_name = _get_model_parallel_group(group_size) # In avoid of the group name too long hashed = hashlib.md5(group_name.encode()).hexdigest()[:48] print(f"Creating hash value for the group_name hash({group_name})={hashed}") group_name = str(hashed) create_group(group_name, group_list) self.allreduce = P.AllReduce(group=group_name) pipeline_group_list, pipeline_group_name = _get_pipeline_group() hashed = hashlib.md5(pipeline_group_name.encode()).hexdigest()[:48] print(f"Creating hash value for the group_name hash({pipeline_group_name})={hashed}") pipeline_group_name = str(hashed) create_group(pipeline_group_name, pipeline_group_list) self.allreduce2 = P.AllReduce(group=pipeline_group_name) else: group_size = get_group_size() self.allreduce_group_size = () for x in params: if "projection.bias" not in x.name and "layernorm" not in x.name and "embedding_table" not in x.name: self.allreduce_group_size = self.allreduce_group_size + (1.0,) elif "embedding_table" not in x.name: self.allreduce_group_size = self.allreduce_group_size + (group_size * 1.0,) else: if not config.word_emb_dp and "position_embedding.embedding_table" not in x.name \ and "top_query_embedding_table" not in x.name: self.allreduce_group_size = self.allreduce_group_size + (config.dp * 1.0,) else: self.allreduce_group_size = self.allreduce_group_size + (group_size * 1.0,) def construct(self, grads): """Calculate global norm construct""" square_sum = self.hyper_map(get_square_sum, grads, self.allreduce_group_size) square_reduce_sum = F.addn(square_sum) if self.is_pipeline: stage_square_reduce_sum = self.allreduce(square_reduce_sum) global_square_reduce_sum = self.allreduce2(stage_square_reduce_sum) global_norms = F.sqrt(global_square_reduce_sum) else: global_norms = F.sqrt(P.AllReduce()(square_reduce_sum)) return global_norms class ClipByGlobalNorm(nn.Cell): """ Clip grads by global norm """ def __init__(self, params, config, clip_norm=1.0): super(ClipByGlobalNorm, self).__init__() self.global_norm = GlobalNorm(params, config) self.clip_norm = Tensor([clip_norm], mstype.float32) self.hyper_map = C.HyperMap() def construct(self, grads): """Clip grads by global norm construct""" global_norm_value = self.global_norm(grads) cond = P.GreaterEqual()(global_norm_value, self.clip_norm) global_norm = F.select(cond, global_norm_value, self.clip_norm) grads = self.hyper_map(F.partial(apply_global_norm, self.clip_norm, global_norm), grads) return grads, global_norm_value class LearningRate(LearningRateSchedule): """ Warmup-decay learning rate for PanguAlpha network. """ def __init__(self, learning_rate, end_learning_rate, warmup_steps, decay_steps, power=1.0, use_cosine=True): super(LearningRate, self).__init__() self.warmup_flag = False if warmup_steps > 0: self.warmup_flag = True self.warmup_lr = WarmUpLR(learning_rate, warmup_steps) self.decay_lr = PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps, power) self.cosine_decay_lr = CosineDecayLR(end_learning_rate, learning_rate, decay_steps) self.warmup_steps = Tensor(np.array([warmup_steps]).astype(np.float32)) self.greater = P.Greater() self.one = Tensor(np.array([1.0]).astype(np.float32)) self.cast = P.Cast() self.use_cosine = use_cosine def construct(self, global_step): """dynamic learning rate""" if not self.use_cosine: decay_lr = self.decay_lr(global_step) else: decay_lr = self.cosine_decay_lr(global_step) if self.warmup_flag: is_warmup = self.cast(self.greater(self.warmup_steps, global_step), mstype.float32) warmup_lr = self.warmup_lr(global_step) lr = (self.one - is_warmup) * decay_lr + is_warmup * warmup_lr else: lr = decay_lr return lr def add_inference_params(opt): """Add inference params""" opt.add_argument("--frequency_penalty", type=float, default=1.5, help="coefficient for frequency_penalty") opt.add_argument("--presence_penalty", type=float, default=0.3, help="coefficient for presence_penalty") opt.add_argument("--max_generate_length", type=int, default=512, help="the maximum number of generated token") opt.add_argument("--top_k", type=int, default=1, help="the number for top_k sampling") opt.add_argument("--top_p", type=float, default=0.9, help="top_p sampling threshold, enabled if less than 1.0, if top_p is set to 1.0, use top_k sampling") opt.add_argument("--end_token", type=int, default=128298, help="the token id for ") opt.add_argument("--use_pynative_op", type=int, default=0, help="Whether use pynative op for postproecess") opt.add_argument("--use_past", type=str, default="true", choices=["true", "false"], help="Whether enable state reuse") opt.add_argument("--input", nargs='+', help="Setting your input sentence") opt.add_argument("--input_file", type=str, default=None, help="Setting your input_file path, sample split by '\n\n' ") opt.add_argument("--output_file", type=str, default=None, help="Setting your output_file path") def add_training_params(opt): """Add training params""" opt.add_argument("--seq_length", type=int, default=1024, help="sequence length, default is 1024.") opt.add_argument("--vocab_size", type=int, default=40000, help="vocabulary size, default is 40000.") opt.add_argument("--embedding_size", type=int, default=16384, help="embedding table size, default is 16384.") opt.add_argument("--num_layers", type=int, default=64, help="total layers, default is 64.") opt.add_argument("--num_heads", type=int, default=128, help="head size, default is 128.") opt.add_argument("--stage_num", type=int, default=1, help="Pipeline stage num, default is 4.") opt.add_argument("--micro_size", type=int, default=1, help="Pipeline micro_size, default is 1.") opt.add_argument("--eod_reset", type=int, default=1, help="Enable eod mask, default is 1.") opt.add_argument("--train_iters", type=int, default=10000, help="Decay step, default is 200000.") opt.add_argument("--warmup_step", type=int, default=200, help="Warmup step, default is 2000.") opt.add_argument("--decay_steps", type=int, default=13000, help="Decay step, default is 200000.") opt.add_argument("--optimizer", type=str, default="adam", choices=["adam", "lamb"], help="select which optimizer to be used, default adam") opt.add_argument("--eod_id", type=int, default=9, help="The id of end of document,6") opt.add_argument("--epoch_size", type=int, default=1, help="The training epoch") opt.add_argument("--sink_size", type=int, default=2, help="The sink size of the training. default is 2") opt.add_argument("--full_batch", default=0, type=int, help="Import the full size of a batch for each card, default is 1") opt.add_argument("--optimizer_shard", type=int, default=1, help="Enable optimizer parallel, default is 1") opt.add_argument("--per_batch_size", type=int, default=4, help="The batch size for each data parallel way. default 6") opt.add_argument("--start_lr", type=float, default=1e-5, help="The start learning rate. default 5e-5")# 5e-6 opt.add_argument("--end_lr", type=float, default=1e-6, help="The end learning rate. default 1e-6")# 5e-7 opt.add_argument("--op_level_model_parallel_num", type=int, default=1, help="The model parallel way. default 8") opt.add_argument("--word_emb_dp", type=int, default=1, choices=[0, 1], help="Whether do data parallel in word embedding. default 1") opt.add_argument("--data_column_name", type=str, default="input_ids", help="Column name of datasets") # 中断恢复配置， pre_trained def add_retrain_params(opt): """ Add parameters about retrain. """ opt.add_argument("--pre_trained", type=ast.literal_eval, default=False, help="Pretrained checkpoint path.") opt.add_argument("--save_checkpoint_path", type=str, default="/cache/ckpt_new", help="Save checkpoint path.") opt.add_argument("--save_checkpoint_bucket_dir", type=str, default="obs://pcl-verify/yizx/distilPangu/easypangu_cache/", help="Save checkpoint path.") opt.add_argument("--ckpt_name_prefix", type=str, default="PanGu-26b-stage1", help="Saving checkpoint name prefix.") opt.add_argument("--base_model_type", type=str, default="alpha", choices=["alpha", "mPangu"], help="Saving checkpoint name prefix.") opt.add_argument("--save_strategy_bucket_dir", type=str, default="s3://research-my/Cloud_compute/model_strategy/strategy/PanGu-26b-m8d1-22m6d20_pd-cmrc-d8-", help="Save checkpoint path.") opt.add_argument("--strategy_load_ckpt_path", type=str, default="s3://research-my/Cloud_compute/model_strategy/strategy/PanGu-26b-m8d1-22m6d20_pd-cmrc-d8-strategy.ckpt", help="The training prallel strategy for the model.") opt.add_argument("--keep_checkpoint_max",## type=int, default=1, help="Max checkpoint save number.") opt.add_argument("--save_checkpoint_steps", type=int, default=1000, help="Save checkpoint step number.") opt.add_argument("--save_checkpoint", type=ast.literal_eval, default=True, help="Whether save checkpoint in local disk.") opt.add_argument("--train_ckpt_name_load", type=str, default="mPanGu53-26b-stage1-2", help="Load checkpoint name prefix.") opt.add_argument("--load_checkpoint_bucket_dir", type=str, default="s3://research-my/Cloud_compute/model_strategy/model/ckpt/together_test/", help="Load checkpoint path, continue trainning.") opt.add_argument("--load_filter_ckpt_bucket_dir", type=str, default="s3://research-my/taoht-13b/filtered_ckpt/mPanGu_53-26b-128k-m1d128-22m1d24/mPanGu_53-26b-128k-m1d128-22m3d26-exp4/exp4-54000/exp4-54000_mPanGu53-26b-stage4_", help="Load checkpoint path, continue trainning.") opt.add_argument("--load_ckpt_num", type=int, default=3250,##30000, help="Load ckpt num steps. 0") opt.add_argument("--has_trained_epoches", type=int, default=0,##30000, help="Epoches has been trained before. 0") opt.add_argument("--has_trained_steps", type=int, default=0,##60000, help="Steps has been trained before. 0") opt.add_argument("--data_start_index", type=int, default=0,##18, help="datasets has been trained before. 0") def get_args(inference=False): """train function for PanguAlpha""" parser = argparse.ArgumentParser(description="PanguAlpha training") parser.add_argument('--device_id', type=int, default=0, help="Device id, default is 0.") parser.add_argument("--device_num", type=int, default=8, help="Use device nums, default is 128.") parser.add_argument("--distribute", type=str, default="true", choices=["true", "false"], help="Run distribute, default is true.") parser.add_argument('--data_url', required=False, default='obs://wiki-test/train_data/', help='Location of data.')## obs://datasets/V2-Multi-langs-CC-21-378G-bpe-128k-jieba/train_simple_corpus/ parser.add_argument('--multi_data_url', help='path to multi dataset', default=WorkEnvironment('train')) parser.add_argument('--train_url', required=False, default=None, help='Location of training outputs.') parser.add_argument("--run_type", type=str, default="train", choices=["train", "predict", "incremental_train", "pre_trained"], help="The run type") parser.add_argument("--model", type=str, default="2B6", choices=["200B", "13B", "2B6", "self_define"], help="The scale of the model parameters") parser.add_argument("--device_target", type=str, default="Ascend", choices=["Ascend", "GPU"], help="The running device") parser.add_argument("--tokenizer_path", type=str, default="./tokenizer_path", help="The path where stores vocab and vocab model file") parser.add_argument("--param_init_type", type=str, default="fp32", help="The initialization type for parameters. Default fp32.") parser.add_argument("--offline", type=int, default=1, help="Running on cloud of not. Default 1.") parser.add_argument("--export", type=int, default=0, help="Whether export mindir for serving.") parser.add_argument("--load_ckpt_name", type=str, default='PanGu_Alpha.ckpt', help="checkpint file name.") # filter_ckpt = 'exp09_GPT3_1-17500_2' # f's3://research-my/taoht-13b/filtered_ckpt/language_enzh/{filter_ckpt}/{filter_ckpt}' parser.add_argument("--load_ckpt_obs_path", type=str,##s3://mindspore-file/huangxinjing/filtered_ckpt/Newexp69_GPT3_5-684_2/Newexp69_GPT3_5-684_2 default='obs://research-my/Cloud_compute/model_strategy/model/cmrc3-pd1-standalone/steps_24000/', help="predict file path.")## parser.add_argument("--load_obs_ckptname", type=str, default='mPanGu53-26b-stage1-2_', help="ckpt file name in obs.")## parser.add_argument("--load_ckpt_local_path", type=str, default='/cache/ckpt_file/', help="incremental training or predict ckpt file path. Default None") parser.add_argument("--incremental_training", type=int, default=0, help="Enable incremental training. Default 0.") parser.add_argument("--src_language", type=str, default="", help="The language to translate") parser.add_argument("--tag_language", type=str, default="", help="trans to tag language") add_training_params(parser) add_retrain_params(parser) if inference: add_inference_params(parser) args_opt = parser.parse_args() return args_opt def download_data(src_data_url, tgt_data_path, rank): """ Download the dataset from the obs. src_data_url (Str): should be the dataset path in the obs tgt_data_path (Str): the local dataset path rank (Int): the current rank id """ cache_url = tgt_data_path EXEC_PATH = '/tmp' if rank % 8 == 0: import moxing as mox print("Modify the time out from 300 to 30000") print("begin download dataset", flush=True) if not os.path.exists(cache_url): os.makedirs(cache_url, exist_ok=True) mox.file.copy_parallel(src_url=src_data_url, dst_url=cache_url) print("Dataset download succeed!", flush=True) f = open("%s/install.txt" % (EXEC_PATH), 'w') f.close() # stop while not os.path.exists("%s/install.txt" % (EXEC_PATH)): time.sleep(1) def ckpt_copy_tar_new(obs_path, target_path="/cache/ckpt"): """ requires the obs_path to be a complete name Copy tar file from the obs to the /cache/ """ sub_name_list = ['part_0.tar', 'part_1.tar', 'part_2.tar', 'part_3.tar'] for item in sub_name_list: sub_name = obs_path + item tmp_name = 'model.tar' mox.file.copy(sub_name, os.path.join(target_path, tmp_name)) os.system('cd {}; tar -xvf {}'.format(target_path, tmp_name)) def get_openi_tar(ckpts_dir): ckpts_with_embedding_npys_list = os.listdir(ckpts_dir) if len(ckpts_with_embedding_npys_list) == 1: assert ckpts_with_embedding_npys_list[0].endswith('ckpt') return os.path.join(ckpts_dir, ckpts_with_embedding_npys_list[0]) else: return None def ckpt_tar_openi(ckpts_dir): """ requires the obs_path to be a complete name Copy tar file from the obs to the /cache/ """ ckpts_with_embedding_npys_list = os.listdir(ckpts_dir) if len(ckpts_with_embedding_npys_list) == 1: assert ckpts_with_embedding_npys_list[0].endswith('ckpt') else: for item in ckpts_with_embedding_npys_list: if item.endswith('.tar'): os.system('cd {}; tar -xvf {}'.format(ckpts_dir, os.path.join(ckpts_dir, item))) os.system('rm -rf {}'.format(os.path.join(ckpts_dir, item))) def get_ckpt_file_list(ckpt_path, device_num): #path = os.listdir(ckpt_path) #print('Get path list:', path, flush=True) returned_list=[] for i in range(0, device_num): returned_list.append('filerted_{}.ckpt'.format(i)) #filtered = [item for item in path if 'embedding' not in item]save_checkpoint_bucket_dir #filtered.sort(key = lambda x: int(x.split('.')[0].split('_')[1])) returned_list = [os.path.join(ckpt_path, item) for item in returned_list if 'embedding' not in item ] print("Sorted list", returned_list) for item in returned_list: fsize = os.path.getsize(item) f_gb = fsize/float(1024)/1024 print(item, " :{:.2f} MB".format(f_gb) ) return returned_list class CheckpointSaveCallback(Callback): def __init__(self, local_ckpt_dir, local_rank=0, has_trained_epoch=0, has_trained_step=0, bucket='obs://ckeckpoint_file/path/', syn_obs_steps=100): self.local_ckpt_dir = local_ckpt_dir self.local_rank = local_rank self.has_trained_epoch = has_trained_epoch self.has_trained_step = has_trained_step if modelarts_flag: self.bucket = bucket self.syn_obs_steps = syn_obs_steps self.obs_save_rank_dir = bucket if not mox.file.exists(self.obs_save_rank_dir): print("Creating ckeckpoint bucket dir {}".format(self.obs_save_rank_dir)) mox.file.make_dirs(self.obs_save_rank_dir) print("entering") else: self.bucket = None self.syn_obs_steps = None self.obs_save_rank_dir = None def step_end(self, run_context): cb_params = run_context.original_args() cur_step = cb_params.cur_step_num + self.has_trained_step if modelarts_flag: if cur_step % self.syn_obs_steps == 0: try: pass obs_save_rank_dir = self.obs_save_rank_dir # + f"steps_{ str(cur_step)}" if not mox.file.exists(obs_save_rank_dir): mox.file.make_dirs(obs_save_rank_dir) print("Copying ckpt to the buckets start", flush=True) mox.file.copy_parallel(self.local_ckpt_dir, obs_save_rank_dir) print("Copying ckpt to the buckets ends", flush=True) except Exception as error: print(error) print('\n###########Upload ckpt Error#############\n') def syn_files(self): if modelarts_flag: process = Process(target=mox.file.copy_parallel, args=(self.local_ckpt_dir, self.bucket), name="file_sync") process.start() class LossSummaryCallback(Callback): def __init__(self, summary_dir, local_rank=0, has_trained_epoch=0, has_trained_step=0, bucket='obs://mindspore-file/loss_file/summary/', syn_times=100): self._summary_dir = summary_dir self.local_rank = local_rank self.has_trained_epoch = has_trained_epoch self.has_trained_step = has_trained_step if modelarts_flag: self.bucket = bucket self.syn_times = syn_times if not mox.file.exists(self.bucket): print("Creating summary bueckt dir {}".format(self.bucket)) mox.file.make_dirs(self.bucket) print("entering") else: self.bucket = None self.syn_times = None self.summary_record = SummaryRecord(self._summary_dir) def step_end(self, run_context): cb_params = run_context.original_args() cur_step = cb_params.cur_step_num + self.has_trained_step # create a confusion matric image, and record it to summary file self.summary_record.add_value('scalar', 'loss', cb_params.net_outputs[0]) self.summary_record.add_value('scalar', 'scale', cb_params.net_outputs[2]) if len(cb_params.net_outputs) >3: self.summary_record.add_value('scalar', 'learning rate', cb_params.net_outputs[3]) if len(cb_params.net_outputs) >4: self.summary_record.add_value('scalar', 'global_norm', cb_params.net_outputs[4]) self.summary_record.record(cur_step) if modelarts_flag: if cur_step % self.syn_times == 0: print("Copying summary to the bueckets start", flush=True) self.summary_record.flush() self.syn_files() print("Copying summary to the bueckets ends", flush=True) def syn_files(self): if modelarts_flag: process = Process(target=mox.file.copy_parallel, args=(self._summary_dir, self.bucket), name="file_sync") process.start() class StrategySaveCallback(Callback): def __init__(self, strategy_path, local_rank=0, has_trained_epoch=0, has_trained_step=0, bucket='obs://mindspore-file/strategy_ckpt/', sym_step=100): self.strategy_path = strategy_path self.local_rank = local_rank self.has_trained_step = has_trained_step self.has_send=False self.file_name = strategy_path.split('/')[-1] if modelarts_flag: self.sym_step = sym_step self.bucket = bucket if not mox.file.exists(self.bucket): print("Creating summary bueckt dir {}".format(self.bucket)) mox.file.make_dirs(self.bucket) else: self.sym_step = None self.bucket = None def step_end(self, run_context): cb_params = run_context.original_args() cur_step = cb_params.cur_step_num + self.has_trained_step if self.has_send is False and modelarts_flag: print("Send strategy file to the obs") self.syn_files() self.has_send = True def syn_files(self): if modelarts_flag: process = Process(target=mox.file.copy_parallel, args=(self.strategy_path, self.bucket + self.file_name), name="file_sync") process.start() pcl_pangu/pcl_pangu/model/panguAlpha_mindspore/train_alpha_ms13.py000066400000000000000000000545311443434453700257600ustar00rootroot00000000000000# Copyright 2022 PCL, Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """ PanguAlpha train script """ import logging import os import math import time from pathlib2 import Path import numpy as np import mindspore as ms from mindspore import context from mindspore.train.model import Model import mindspore.communication.management as D from mindspore.context import ParallelMode import mindspore.nn as nn from mindspore.train.callback import TimeMonitor, Callback from mindspore.nn.wrap.loss_scale import DynamicLossScaleUpdateCell import mindspore.common.dtype as mstype from mindspore.parallel import set_algo_parameters from mindspore.parallel._cost_model_context import _set_multi_subgraphs from mindspore.nn.wrap.cell_wrapper import PipelineCell, _VirtualDatasetCell from mindspore.train.callback import ModelCheckpoint, CheckpointConfig from mindspore.train.serialization import load_checkpoint, load_param_into_net from src.dataset import create_dataset from src.pangu_alpha import PanguAlpha, PanguAlphaWithLoss, CrossEntropyLoss # from src.pangu_alpha_raw import PanguAlphaModel as PanguAlpha # from src.pangu_alpha_raw import PanGuAlphaWithLoss as PanguAlphaWithLoss # from src.pangu_alpha_raw import CrossEntropyLoss from src.pangu_alpha_wrapcell import PanguAlphaTrainOneStepWithLossScaleCell from src.pangu_alpha_config import PANGUALPHAConfig, set_parse from src.utils_pangu import LearningRate, get_args, FP32StateAdamWeightDecay from src.utils_pangu import download_data, get_openi_tar, ckpt_tar_openi, ckpt_copy_tar_new, get_ckpt_file_list from src.utils_pangu import StrategySaveCallback, CheckpointSaveCallback try: import moxing as mox modelarts_flag = True except: modelarts_flag = False from mindspore import Parameter import mindspore.ops as ops project_root = os.path.abspath( os.path.dirname(os.path.realpath(__file__)) + os.path.sep + "..") print('project_root:', project_root) class LossCallBack(Callback): """ Monitor the loss in training. If the loss in NAN or INF terminating training. """ def __init__(self, dataset_size=-1, local_rank=0, has_trained_epoch=0, has_trained_step=0, micro_size=1): super(LossCallBack, self).__init__() self._dataset_size = dataset_size self.local_rank = local_rank self.has_trained_epoch = has_trained_epoch self.has_trained_step = has_trained_step self.micro_size = micro_size print("load has trained epoch :{} and step: {}".format(has_trained_epoch, has_trained_step), flush=True) def step_end(self, run_context): """ Print loss after each step """ cb_params = run_context.original_args() if self._dataset_size > 0 and self.local_rank % 8 == 0: percent, epoch_num = math.modf(cb_params.cur_step_num / self._dataset_size) if percent == 0: epoch_num -= 1 date = time.asctime(time.localtime(time.time())) loss_value = cb_params.net_outputs[0].asnumpy() / self.micro_size D.init() rank = D.get_rank() log_local_tmp = "/cache/train_log_node.txt" if rank%8 == 0: lines = "time: {} local_rank: {}, epoch: {}, step: {}, loss is {}, overflow is {}, scale is {}, lr is {}".format(date, int(self.local_rank), int(epoch_num) + int(self.has_trained_epoch), cb_params.cur_step_num + int(self.has_trained_step), loss_value, cb_params.net_outputs[1].asnumpy(), cb_params.net_outputs[2].asnumpy(), cb_params.net_outputs[3].asnumpy()) print(lines) def add_checkpoint_callback_policy(args_param, callback, rank_id): r""" Add checkpoint policy to callback. """ if args_param.save_checkpoint: if not os.path.exists(args_param.save_checkpoint_path): os.makedirs(args_param.save_checkpoint_path, exist_ok=True) if modelarts_flag: if not mox.file.exists(args_param.train_url): mox.file.make_dirs(args_param.train_url) if not mox.file.exists(os.path.join(args_param.train_url, 'strategy_ckpt')): mox.file.make_dirs(os.path.join(args_param.train_url, 'strategy_ckpt')) else: if not os.path.exists(os.path.join(args_param.train_url, 'strategy_ckpt')): os.makedirs(os.path.join(args_param.train_url, 'strategy_ckpt', exist_ok=True)) logging.INFO("> saving strategy_ckpt to path: {}".format(os.path.join(args_param.train_url, 'strategy_ckpt'))) # checkpoint store epoch_num and step_num info ckpt_append_info = [{"epoch_num": args_param.has_trained_epoches, "step_num": args_param.has_trained_steps}] ckpt_config = CheckpointConfig(save_checkpoint_steps=args_param.save_checkpoint_steps, keep_checkpoint_max=5, integrated_save=False, append_info=ckpt_append_info ) save_dir_rank = os.path.join(args_param.save_checkpoint_path, f"rank_{rank_id}") if args_param.model == '350M': args_param.ckpt_name_prefix = args_param.base_model_type + '-350M' elif args_param.model == '2B6': args_param.ckpt_name_prefix = args_param.base_model_type + '-2B6' elif args_param.model == '13B': args_param.ckpt_name_prefix = args_param.base_model_type + '-13B' else: raise ImportError save_ckptfile_name = args_param.ckpt_name_prefix + '_' + str(rank_id) if not os.path.exists(save_dir_rank): os.makedirs(save_dir_rank, exist_ok=True) print(">>> save_checkpoint_steps: {}, train_url: {}, save_dir: {}, save_name: {}>>>".format(args_param.save_checkpoint_steps, args_param.train_url, save_dir_rank, save_ckptfile_name)) ckpoint_cb = ModelCheckpoint(prefix=save_ckptfile_name, directory=save_dir_rank, config=ckpt_config) # # need to compress mp_parellel_model to OneCKPT!!!! Disable OBS SYNC during Training! # ckpt_save_obs_cb = CheckpointSaveCallback(local_ckpt_dir=save_dir_rank, # local_rank=rank_id, # has_trained_epoch=args_param.has_trained_epoches, # has_trained_step=args_param.has_trained_steps, # bucket=args_param.train_url, # syn_obs_steps=args_param.save_checkpoint_steps # ) callback.append(ckpoint_cb) # callback.append(ckpt_save_obs_cb) if rank_id == 0: sub_dir = args_param.save_checkpoint_bucket_dir.split('/')[-1] callback.append(StrategySaveCallback(strategy_path='/cache/strategy.ckpt', local_rank=0, has_trained_epoch=args_param.has_trained_epoches, has_trained_step=args_param.has_trained_steps, bucket=os.path.join(args_param.train_url, 'strategy_ckpt/'))) def load_train_net(args_opt): r""" The main training process. """ # Set hccl connect time os.environ['HCCL_CONNECT_TIMEOUT'] = "7200" # Set execution mode context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target) context.set_context(variable_memory_max_size="30GB") print(args_opt) # Set parallel context if args_opt.distribute == "true": D.init() device_num = D.get_group_size() rank = D.get_rank() print("rank_id is {}, device_num is {}".format(rank, device_num)) context.reset_auto_parallel_context() if args_opt.incremental_training: local_strategy_ckpt_path = args_opt.strategy_load_ckpt_path if args_opt.device_num > 64: if local_strategy_ckpt_path is None: context.set_auto_parallel_context( parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL, gradients_mean=False, full_batch=bool(args_opt.full_batch), enable_parallel_optimizer=bool(args_opt.optimizer_shard), optimizer_weight_shard_size=64, strategy_ckpt_save_file='/cache/strategy.ckpt') else: context.set_auto_parallel_context( parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL, gradients_mean=False, full_batch=bool(args_opt.full_batch), strategy_ckpt_load_file=local_strategy_ckpt_path, enable_parallel_optimizer=bool(args_opt.optimizer_shard), optimizer_weight_shard_size=64, strategy_ckpt_save_file='/cache/strategy.ckpt') else: if local_strategy_ckpt_path is None: context.set_auto_parallel_context( parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL, gradients_mean=False, full_batch=bool(args_opt.full_batch), enable_parallel_optimizer=bool(args_opt.optimizer_shard), optimizer_weight_shard_aggregated_save=True, strategy_ckpt_save_file='/cache/strategy.ckpt') else: context.set_auto_parallel_context( parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL, gradients_mean=False, full_batch=bool(args_opt.full_batch), strategy_ckpt_load_file=local_strategy_ckpt_path, enable_parallel_optimizer=bool(args_opt.optimizer_shard), optimizer_weight_shard_aggregated_save=True, strategy_ckpt_save_file='/cache/strategy.ckpt') else: if args_opt.device_num > 64: context.set_auto_parallel_context( parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL, gradients_mean=False, full_batch=bool(args_opt.full_batch), enable_parallel_optimizer=bool(args_opt.optimizer_shard), optimizer_weight_shard_size=64, strategy_ckpt_save_file='/cache/strategy.ckpt') else: context.set_auto_parallel_context( parallel_mode=ParallelMode.SEMI_AUTO_PARALLEL, gradients_mean=False, full_batch=bool(args_opt.full_batch), enable_parallel_optimizer=bool(args_opt.optimizer_shard), optimizer_weight_shard_aggregated_save=True, strategy_ckpt_save_file='/cache/strategy.ckpt') set_algo_parameters(elementwise_op_strategy_follow=True) _set_multi_subgraphs() else: rank = 0 device_num = 1 context.set_context(save_graphs=False, save_graphs_path="./graphs_of_device_id_" + str(rank)) ################################################### ## context.set_context(enable_graph_kernel=True) ################################################### # copy data from the cloud to the /cache/Data cache_url = "/cache/Data/" if args_opt.offline: cache_url = args_opt.data_url else: raise ImportError # download_data(src_data_url=args_opt.data_url, tgt_data_path=cache_url, rank=rank) # Set model property model_parallel_num = args_opt.op_level_model_parallel_num assert device_num >= model_parallel_num, '> device_num must bigger than model_parallel_num' data_parallel_num = int(device_num / model_parallel_num) batch_size = args_opt.per_batch_size #* data_parallel_num assert batch_size >= data_parallel_num, '> global batch_size must bigger than data_parallel_num' if len(os.listdir(args_opt.load_ckpt_local_path)) <= 1: local_npy_path = None else: local_npy_path = args_opt.load_ckpt_local_path config = PANGUALPHAConfig( data_parallel_num=data_parallel_num, model_parallel_num=model_parallel_num, batch_size=batch_size, seq_length=args_opt.seq_length, vocab_size=args_opt.vocab_size, embedding_size=args_opt.embedding_size, num_layers=args_opt.num_layers, num_heads=args_opt.num_heads, expand_ratio=4, dropout_rate=0.1, compute_dtype=mstype.float16, stage_num=args_opt.stage_num, micro_size=args_opt.micro_size, eod_reset=bool(args_opt.eod_reset), load_ckpt_path=local_npy_path, param_init_type=mstype.float32 if args_opt.param_init_type == 'fp32' else mstype.float16, word_emb_dp=bool(args_opt.word_emb_dp)) print("===config is: ", config, flush=True) # Define network pangu_alpha = PanguAlpha(config) loss = CrossEntropyLoss(config) pangu_alpha_with_loss = PanguAlphaWithLoss(config, pangu_alpha, loss) pangu_alpha_with_loss = _VirtualDatasetCell(pangu_alpha_with_loss) # Warm-up and cosine decay learning rate lr = LearningRate(learning_rate=args_opt.start_lr, end_learning_rate=args_opt.end_lr, warmup_steps=args_opt.warmup_step, decay_steps=args_opt.decay_steps) # Set weight decay coefficient, zero for bias and layernorm, 1e-1 for rest decay_filter = lambda x: 'layernorm' not in x.name.lower() and "bias" not in x.name.lower() params = pangu_alpha.trainable_params() decay_params = list(filter(decay_filter, params)) other_params = list(filter(lambda x: not decay_filter(x), params)) group_params = [{ 'params': decay_params, 'weight_decay': 1e-1 }, { 'params': other_params, 'weight_decay': 0.0 }, { 'order_params': params }] if args_opt.optimizer == "lamb": optimizer = nn.Lamb(group_params, learning_rate=lr) else: optimizer = FP32StateAdamWeightDecay(group_params, learning_rate=lr, eps=1e-8, beta1=0.9, beta2=0.95)## 0.95 # Initial scaling sens loss_scale_value = math.pow(2, 16) ################################################################# callback_size = args_opt.sink_size if args_opt.distribute == "true": D.init() device_num = D.get_group_size() rank = D.get_rank() if args_opt.pre_trained: callback = [ TimeMonitor(callback_size), LossCallBack(callback_size, rank, args_opt.has_trained_epoches, args_opt.has_trained_steps) ] else: callback = [ TimeMonitor(callback_size), LossCallBack(callback_size, rank, 0, 0)] ################################################################## update_cell = DynamicLossScaleUpdateCell(loss_scale_value=loss_scale_value, scale_factor=2, scale_window=100) pangu_alpha_with_grads = PanguAlphaTrainOneStepWithLossScaleCell( pangu_alpha_with_loss, optimizer=optimizer, scale_update_cell=update_cell, enable_global_norm=True, config=config) model = Model(pangu_alpha_with_grads) epoch_num = 1 #args_opt.epoch_size # Dataset loading mindrecord files ds = create_dataset(config.batch_size, data_path=cache_url, data_start_index=0, #args_opt.data_start_index, eod_reset=config.eod_reset, full_batch=bool(args_opt.full_batch), eod_id=args_opt.eod_id, device_num=device_num, rank=rank, column_name=args_opt.data_column_name, epoch=epoch_num) return pangu_alpha, args_opt, model, ds, callback, config def restore_filtered_ckpt_from_obs_incremental_training(args_opt, model): r""" Load checkpoint process. """ rank = D.get_rank() OneCKPTPath = get_openi_tar(args_opt.load_ckpt_local_path) if args_opt.incremental_training: local_strategy_ckpt_path="/cache/strategy.ckpt" if rank % 8 == 0: print("Incremental training", flush=True) os.system('ulimit -s 102400') ckpt_tar_openi(args_opt.load_ckpt_local_path) print("setting env success.") # 下载模型文件结束后，写一个文件来表示下载成功 f = open("/cache/get_ckpts.txt", 'w') f.close() if args_opt.incremental_training: # 此处用于阻塞其他进程，直到刷包以及下载数据集完成为止 while not os.path.exists("/cache/get_ckpts.txt"): time.sleep(1) print("\n\n************Checkpoint download succeed!*************\n\n", flush=True) if rank % 8 == 0: print(os.listdir(args_opt.load_ckpt_local_path), flush=True) if args_opt.incremental_training: if OneCKPTPath is None: from mindspore.train.serialization import load_distributed_checkpoint if args_opt.model == '2B6': device_num = 512 elif args_opt.model == '13B': device_num = 512 else: raise ImportError('> Only Support 2B6 / 13B fine_tuning') ckpt_file_list = get_ckpt_file_list(args_opt.load_ckpt_local_path, device_num=device_num) print("Start to load distributed checkpoint", flush=True) print(f"Loading from path {ckpt_file_list[0]}", flush=True) load_distributed_checkpoint(model.train_network, ckpt_file_list) else: print("Start to load one-ckpt checkpoint", flush=True) print(f"Loading from path {OneCKPTPath}", flush=True) parameters = load_checkpoint(OneCKPTPath) load_param_into_net(model.train_network, parameters) def main(opt): os.environ['HCCL_CONNECT_TIMEOUT'] = "7200" net, args_opt, model, ds, callback, config = load_train_net(opt) D.init() device_num = D.get_group_size() rank_id = D.get_rank() model_parallel_num = args_opt.op_level_model_parallel_num assert device_num >= model_parallel_num, '> device_num must bigger than model_parallel_num' data_parallel_num = int(device_num / model_parallel_num) batch_size = args_opt.per_batch_size # * data_parallel_num assert batch_size >= data_parallel_num, '> global batch_size must bigger than data_parallel_num' step_per_epoch = ds.get_dataset_size() callback_size = args_opt.sink_size actual_epoch_num = int(step_per_epoch / callback_size) print("\n=====dataset size: ", ds.get_dataset_size(), flush=True) print("=====batchsize: ", batch_size, flush=True) print("=====actual_epoch_num: ", actual_epoch_num, flush=True) print(f"=====mp: {model_parallel_num}, dp: {data_parallel_num}\n") ################################################################# add_checkpoint_callback_policy(args_opt, callback, rank_id) # 变策略迁移学习 if args_opt.incremental_training: restore_filtered_ckpt_from_obs_incremental_training(args_opt, model) # 初始化，从头学习 else: model._init(train_dataset=ds, sink_size=2) print("===== training {} epochs of dataset!!! ======".format(args_opt.train_iters / float(actual_epoch_num))) print("=====[{}/{}]: train_iters / actual_1epoch_iters: ".format(args_opt.train_iters, actual_epoch_num), flush=True) model.train(args_opt.train_iters, ds, callbacks=callback, sink_size=callback_size, dataset_sink_mode=True) def find_local_ckpt_maxStepNumber(save_dir_rank): tmp_items = os.listdir(save_dir_rank) steps_numbers = [] for item in tmp_items: if item.endswith('.ckpt') and 'alpha-2B6' in item: step_number = int(item.split('_2.ckpt')[0].split('-')[-1]) steps_numbers.append(step_number) return np.max(steps_numbers) def setup_args(args_opt, model_config_dict): args_opt.op_level_model_parallel_num = model_config_dict.get('model_parallel_size') args_opt.per_batch_size = model_config_dict.get('batch_size') args_opt.model = model_config_dict.get('model') args_opt.seq_length = model_config_dict.get('seq_length') args_opt.embedding_size = model_config_dict.get('hidden_size') args_opt.num_layers = model_config_dict.get('num_layers') + 1 args_opt.num_heads = model_config_dict.get('num_attention_heads') args_opt.start_lr = model_config_dict.get('start_lr') args_opt.end_lr = model_config_dict.get('end_lr') args_opt.train_iters = model_config_dict.get('train_iters') args_opt.decay_steps = model_config_dict.get('lr_decay_iters') args_opt.save_checkpoint_steps = model_config_dict.get('save_checkpoint_steps') args_opt.strategy_load_ckpt_path = model_config_dict.get('strategy_load_ckpt_path') args_opt.save_checkpoint_path = model_config_dict.get('save') args_opt.load_ckpt_local_path = model_config_dict.get('load') args_opt.data_url = model_config_dict.get('data_path') args_opt.vocab_size = model_config_dict.get('vocab_size') args_opt.incremental_training = model_config_dict.get('finetune') return args_opt def alpha_add_args(args_opt): args_opt.base_model_type = 'alpha' return args_opt def mPangu_add_args(args_opt): args_opt.base_model_type = 'mPangu' return args_opt opt = get_args() # set_parse(opt) # obs_upload_dir = 'ckpt_pd_cmrc' # main(opt, obs_upload_dir) pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/000077500000000000000000000000001443434453700217415ustar00rootroot00000000000000pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/3-minus-inference-en.md000066400000000000000000000041511443434453700261130ustar00rootroot00000000000000# 3 minutes inference tutorial for PanGu-α model You can apply for a T4 GPU on the Cloud Brain page under the project in the OpenI community. # 1、Register for OpenI Community This step is not detailed, it's simple. Sight in OpenI community. # 2、Fork PanGu-Alpha-GPU If you want to apply for a GPU in the OpenI community you have to own a project first, and then apply for T4 inside the project. So you need to make [PanGu-Alpha-GPU](https://openi.pcl.ac.cn/PCL-Platform.Intelligence/PanGu-Alpha-GPU) as your personal project. Go to the [PanGu-Alpha-GPU](https://openi.pcl.ac.cn/PCL-Platform.Intelligence/PanGu-Alpha-GPU) project and click the `derive` button in the upper right corner to derive your own project based on the original one. # 3. Apply GPU - Click `云脑` in the project ![img.png](images/choice-yunnao.png) - Click`新建任务` - Search for the following image in the `镜像` section ![img.png](images/chice-images.png) - Select 'PanguAlpha_2.6B_fp16_mgt.zip' in the `数据集存放路径` field ![img.png](images/choice-dataset.png) - Click`新建任务` - Wait a while and the `调试` button will appear, click inside you will see the familiar jupyter debugging interface # 4、inference - Directory structure /dataset/ where the dataset is stored, we put the model in this directory when we just submitted the task /code/ where the code of your project will be copied to this directory /model/ where the model is stored, you can download the files in this directory via `model download` in the task submission screen - Go to the `/code/panguAlpha_pytorch` directory - Run the inference script ```bash python tools/generate_samples_Pangu.py \ --model-parallel-size 1 \ --num-layers 31 \ --hidden-size 2560 \ --load /dataset/Pangu-alpha_2.6B_mgt/ \ --num-attention-heads 32 \ --max-position-embeddings 1024 \ --tokenizer-type GPT2BPETokenizer \ --fp16 \ --batch-size 1 \ --seq-length 1024 \ --out-seq-length 50 \ --temperature 1.0 \ --vocab-file megatron/tokenizer/bpe_4w_pcl/vocab \ --num-samples 0 \ --top_k 2 \ --finetune ``` Congratulations, you have completed the inference processpcl_pangu/pcl_pangu/model/panguAlpha_pytorch/3-minus-inference.md000066400000000000000000000042301443434453700255110ustar00rootroot00000000000000# 3分钟实现盘古α模型推理 启智社区中项目下的云脑页面可以申请一张 T4 GPU，没有服务器的个人或学生可以在启智社区上快速免费体验盘古α。本教程将手把手教你3分钟实现盘古α的推理流程。 # 1、注册启智社区账号 这一步就不细说了，无非就是邮箱、账号、密码。 用申请的账号登录到启智社区。 # 2、派生 PanGu-Alpha-GPU 项目 如果你想申请启智社区的 GPU 就必须先有项目，然后在项目里面申请 T4。所以需要把 [PanGu-Alpha-GPU](https://openi.pcl.ac.cn/PCL-Platform.Intelligence/PanGu-Alpha-GPU) 派生成为你个人的项目。 进入[PanGu-Alpha-GPU](https://openi.pcl.ac.cn/PCL-Platform.Intelligence/PanGu-Alpha-GPU) 项目，点击右上角的`派生`按钮，在原项目基础上派生出自己的项目。 # 3、申请GPU - 点击项目中的`云脑` ![img.png](images/choice-yunnao.png) - 点击`新建任务` - 在`镜像`那一栏里面搜索下图的镜像 ![img.png](images/chice-images.png) - 在`数据集存放路径`那一栏选 'PanguAlpha_2.6B_fp16_mgt.zip' ![img.png](images/choice-dataset.png) - 点击`新建任务` - 等待一段时间就会出现 `调试` 按钮，点击进去就是熟悉的 jupyter 调试界面了 # 4、推理 - 目录结构 /dataset/ 存放数据集的地方，刚刚提交任务的时候我们把模型放在该目录下 /code/ 存放代码的地方，你项目中的代码会拷贝到该目录下 /model/ 存放模型的地方，在任务提交界面可以通过`模型下载`下载该目录下的文件 - 进入 /code/panguAlpha_pytorch 目录 - 运行推理脚本 ```bash python tools/generate_samples_Pangu.py \ --model-parallel-size 1 \ --num-layers 31 \ --hidden-size 2560 \ --load /dataset/Pangu-alpha_2.6B_mgt/ \ --num-attention-heads 32 \ --max-position-embeddings 1024 \ --tokenizer-type GPT2BPETokenizer \ --fp16 \ --batch-size 1 \ --seq-length 1024 \ --out-seq-length 50 \ --temperature 1.0 \ --vocab-file megatron/tokenizer/bpe_4w_pcl/vocab \ --num-samples 0 \ --top_k 2 \ --finetune ``` 恭喜你，完成了推理流程pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/LICENSE000066400000000000000000000341371443434453700227560ustar00rootroot00000000000000The following applies to all files unless otherwise noted: # Copyright (c) 2020, NVIDIA CORPORATION. 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Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/MANIFEST.in000066400000000000000000000001011443434453700234670ustar00rootroot00000000000000include megatron/data/Makefile include megatron/data/helpers.cpp pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/README-en.md000066400000000000000000000204041443434453700236200ustar00rootroot00000000000000This is a pytorch implementation of the Pangu alpha model. It can be inferred, trained, and finetune on the pytorch framework. Starting point: Mindspore is a new deep learning framework that many people have not used, so converting mindspore models to pytorch models will allow more people to use our Pangu models and allow users to not only experience our large models, but also finetune our models. Megatron is a large, powerful transformer algorithm library developed by NVIDIA's deep learning applications research team. This port is based on Megatron, and the main work includes converting model files, adding query layer, and modifying model slicing strategy. # Environments Supports python >= 3.6, pytorch >= 1.5, cuda >= 10, and nccl >= 2.6 versions. The official NVIDIA docker image `docker pull nvcr.io/nvidia/pytorch:20.03-py3` is recommended. You need to install [NLTK](https://www.nltk.org/install.html). You can also download the paired image directly at ```bash docker pull yands/pangu-alpha-megatron-lm-nvidia-pytorch:20.03.2 ``` Using`/opt/conda/bin/python`。 # Model File Download | Model File | Md5 | Size | Parameter Configuration | | ------------------------------------------------------------ | -------------------------------- | ---- | ------------------------------------------------------------ | | [Pangu-alpha_2.6B_fp16_mgt.zip](https://openi.pcl.ac.cn/attachments/72aec03d-6bdb-4652-ac2a-8099db4b0bed) | 28f6dd2ec5d1df2fd22ec5f4a66f51e7 | 4.6G | num-layers : 31
    hidden-size : 2560
    num-attention-heads : 32 | | [Pangu-alpha_13B_fp16_mgt.zip](https://openi.pcl.ac.cn/attachments/937b3e2d-98fb-4871-9691-b32afb5a4d79?type=0) | e6f7a05cbdf8ba8d69e6786e48344f6f | 22G | num-layers : 39
    hidden-size : 5120
    num-attention-heads : 40 | **Note：`num-layers` is equal to `num-layers - 1` in [Pangu](https://openi.pcl.ac.cn/PCL-Platform.Intelligence/PanGu-Alpha)** Model file directory structure. ```txt Pangu-alpha_2.6B_fp16_mgt #model directory, --load parameter needs to fill in the path -- iter_0001000 # iteration number directory --mp_rank_00 # directory for each GPU when the model is parallel --model_optim_rng.pt #model file --latest_checkpointed_iteration.txt #file of iterations of ckpt ``` # Accuracy There are some differences in the results of the `mean` operator between the two frameworks, resulting in inconsistent output of the `LayerNorm` layer, so the generated results are not exactly consistent. Not solved yet, looking for a solution :-). On the iflytek task, the few-shot accuracy of pytorch's 2.6b_fp16 model is 0.78929, which is 2 points down from the paper's 0.81. # Inference ###** Want a quick experience? Check out the [3-Minute Tutorial on inference](3-minus-inference-en.md)! You can white-knuckle the T4 server!!! ** Currently only the inference script for generating text is available, as follows. Requires the following configuration parameters. `-out-seq-length`: the maximum number of tokens to generate `--top_k`: the larger the value of k, the higher the diversity of generated samples. Different k can be tried. ```bash python tools/generate_samples_Pangu.py \ --model-parallel-size 1 \ --num-layers 31 \ --hidden-size 2560 \ --load /dataset/Pangu-alpha_2.6B_mgt/ \ --num-attention-heads 32 \ --max-position-embeddings 1024 \ --tokenizer-type GPT2BPETokenizer \ --fp16 \ --batch-size 1 \ --seq-length 1024 \ --out-seq-length 50 \ --temperature 1.0 \ --vocab-file megatron/tokenizer/bpe_4w_pcl/vocab \ --num-samples 0 \ --top_k 2 \ --finetune ``` Examples： k=1 ```txt Input is: 书生：羌笛何须怨杨柳，春风不度玉门关。 飞云：（这诗怎么这么耳熟？且过去跟他聊聊如何。） 书生：小兄弟，要不要一起喝一杯？ 飞云：你请我呀？你若是请我，我便和你喝一杯；你若不请我，我便一个人去喝。 书生：小兄弟，看你年纪轻轻，不至于这么势利吧？ 飞云： Output is: 我是个读书人,不势利,我只想和你喝酒。 书生:小兄弟,你是不是有什么心事? Input is: 张无忌拿出屠龙宝刀，手起刀落，周芷若掉了一颗门牙，身旁的赵敏喜极而泣， Output is: 张无忌道:“你这是做甚么?”赵敏道:“我要你做我的丈夫。”张无忌道:“我不配。”赵敏道:“你不配,我也不配 Input is: 四川的省会是? Output is: 成都,简称蓉,别称“锦城”,是四川省省会,也是中国西部地区重要的中心城市,中国西部地区重要的经济中心、科技中心、金融中心和交通枢纽,中国西部地区重要的 ``` k=2 ```txt Input is: 书生：羌笛何须怨杨柳，春风不度玉门关。 飞云：（这诗怎么这么耳熟？且过去跟他聊聊如何。） 书生：小兄弟，要不要一起喝一杯？ 飞云：你请我呀？你若是请我，我便和你喝一杯；你若不请我，我便一个人去喝。 书生：小兄弟，看你年纪轻轻，不至于这么势利吧？ 飞云： Output is: 你这是在质疑本公子的人格?你若是不请我喝酒我就不去喝。你若不请我喝,那我就一个人走路。你要是不请我喝,那你便 ``` k = 3 ```txt Input is: 中国和美国和日本和法国和加拿大和澳大利亚的首都分别是哪里？ Output is: 分别在哪个省的哪个市 中国的北京,美国纽约,加拿大多伦多,日本的大阪,法国的里昂,澳大利亚的墨尔本,新西兰的基督城,澳大利亚首都堪 ``` # Finetune Currently only finetune is provided without changing the model model structure and data format, i.e. continue pre-training. ##### 1. Preparing training data Refer to [data](#data) section ##### 2. Model cutting The model downloaded above is a single machine inference model, so you need to cut the model first when finetune is performed, and cut it into model parallel models. Parameters. `-model-parallel-size`: the number of slices of the original model, here is 1 `--num-mp-model`: the number of models after slicing `--mp-model-save`: the path to save the model after slicing ```bash python tools/split_full_model_into_mp_model.py \ --model-parallel-size 1 \ --num-mp-model 2 \ --num-layers 31 \ --hidden-size 2560 \ --load /**ful model path**/ \ --mp-model-save /**mp model save path**/ \ --num-attention-heads 32 \ --max-position-embeddings 1024 \ --tokenizer-type GPT2BPETokenizer \ --fp16 \ --batch-size 1 \ --seq-length 1024 \ --model-type Pangu \ --vocab-file megatron/tokenizer/bpe_4w_pcl/vocab \ --finetune ``` ##### 3. Training Run the script: ```examples/finetune_pangu_distributed.sh``` ##### 4. Model merging The finished model of finetune is fragmented, so if you want to do single card inference, you need to merge the model first. Merge script. `--mp-model-parallel-size`: the number of model slices `--load`: model save directory ```bash python tool/merge_mp_partitions.py \ --model-parallel-size 1 \ --mp-model-parallel-size 2 \ --num-layers 31 \ --hidden-size 2560 \ --load /full model ckpt dir/ \ --num-attention-heads 32 \ --max-position-embeddings 1024 \ --tokenizer-type GPT2BPETokenizer \ --fp16 \ --batch-size 1 \ --seq-length 1024 \ --model-type Pangu \ --vocab-file megatron/tokenizer/bpe_4w_pcl/vocab \ --reset-attention-mask \ --finetune \ ``` # Training Reference Script ```bash examples/pretrain_pangu_distributed_2.6B.sh ``` # Data ##### Generate training data Reference script: `/tools/preprocess_data_pangu.py` Store multiple `xxx.txt` files in the train_dataset directory, if there are more training data, it is better to have a uniform file size for each `txt` and separate multiple `txt`s, the size can be 10M a file. If there is traditional text that needs to be converted to simplified, you can use `zhconv`. The format of each `txt` text is (need blank lines to split different samples) ```txt sample 1 *** *** *** sample 2 *** *** *** sample 2 *** *** *** ``` ```bash python /tools/preprocess_data_pangu.py \ --input /train_dataset/*.txt \ --output-prefix /megatron/dataset/ \ --vocab-file /megatron/tokenizer/bpe_4w_pcl/vocab \ --dataset-impl mmap \ --append-eod ``` The files /path/to/dataset/xxx.idx and /path/to/dataset/xxx.bin will be generated. Finetune and pre-training require the parameter: `-data-path=/path/to/dataset/xxx` pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/README.md000066400000000000000000000176471443434453700232370ustar00rootroot00000000000000这是盘古α模型的 pytorch 实现版本。可以在 pytorch 框架上进行推理、训练、finetune。 出发点：Mindspore 是新的深度学习框架，很多人没用过，所以把 mindspore 模型转成 pytorch 模型可以让更多人使用我们的盘古模型，让使用者不但可以体验我们的大模型，还可以对我们的模型进行 finetune 。 Megatron 是英伟达深度学习应用研究团队开发的一款大型、强大的 transformer 算法库。这次的移植是在 Megatron 的基础上修改得到，主要工作内容包括了模型文件的转换、增加 query layer、修改模型切分策略。 # 环境 支持 python >= 3.6, pytorch >= 1.5, cuda >= 10, and nccl >= 2.6 版本. 推荐使用英伟达的官方 docker 镜像`docker pull nvcr.io/nvidia/pytorch:20.03-py3`。需要安装 [NLTK](https://www.nltk.org/install.html)。 也可直接下载配好的镜像： ```bash docker pull yands/pangu-alpha-megatron-lm-nvidia-pytorch:20.03.2 ``` 使用`/opt/conda/bin/python`。 # 模型文件下载 | 模型文件 | Md5 | 大小 | 参数配置 | | ------------------------------------------------------------ | -------------------------------- | ---- | ------------------------------------------------------------ | | [Pangu-alpha_2.6B_fp16_mgt.zip](https://openi.pcl.ac.cn/attachments/72aec03d-6bdb-4652-ac2a-8099db4b0bed) | 28f6dd2ec5d1df2fd22ec5f4a66f51e7 | 4.6G | num-layers : 31
    hidden-size : 2560
    num-attention-heads : 32 | | [Pangu-alpha_13B_fp16_mgt.zip](https://openi.pcl.ac.cn/attachments/937b3e2d-98fb-4871-9691-b32afb5a4d79?type=0) | e6f7a05cbdf8ba8d69e6786e48344f6f | 22G | num-layers : 39
    hidden-size : 5120
    num-attention-heads : 40 | **注：`num-layers` 等于 [Pangu](https://openi.pcl.ac.cn/PCL-Platform.Intelligence/PanGu-Alpha) 项目中的 `num-layers - 1`** 模型文件目录结构： ```txt Pangu-alpha_2.6B_fp16_mgt #模型目录，--load 参数需要填写的路径 -- iter_0001000 #迭代次数目录 --mp_rank_00 #模型并行时各个 GPU 的目录 --model_optim_rng.pt #模型文件 --latest_checkpointed_iteration.txt #记录 ckpt 的迭代次数文件 ``` # 精度 两个框架的`mean`算子的结果有一定的差异，导致 `LayerNorm` 层的输出不一致，所以生成结果不完全一致。暂时还没解决，正在寻找解决方案 :-) 。 在 iflytek 任务上，pytorch 的 2.6b_fp16 模型的 few-shot 精度为0.78929，相对于论文的的0.81 下降了 2 个点。 # 推理 ###**想快速体验？？请查看[3分钟实现推理教程](3-minus-inference.md)！！可以白嫖 T4 服务器哦！！！** 目前只有生成文本的推理脚本，如下： 需要配置参数： `--out-seq-length`：生成的最大 token 数 `--top_k`：k 值越大生成样本多样性越高。可以尝试不同的 k。 ```bash python tools/generate_samples_Pangu.py \ --model-parallel-size 1 \ --num-layers 31 \ --hidden-size 2560 \ --load /dataset/Pangu-alpha_2.6B_mgt/ \ --num-attention-heads 32 \ --max-position-embeddings 1024 \ --tokenizer-type GPT2BPETokenizer \ --fp16 \ --batch-size 1 \ --seq-length 1024 \ --out-seq-length 50 \ --temperature 1.0 \ --vocab-file megatron/tokenizer/bpe_4w_pcl/vocab \ --num-samples 0 \ --top_k 2 \ --finetune ``` 例子： k=1 ```txt Input is: 书生：羌笛何须怨杨柳，春风不度玉门关。 飞云：（这诗怎么这么耳熟？且过去跟他聊聊如何。） 书生：小兄弟，要不要一起喝一杯？ 飞云：你请我呀？你若是请我，我便和你喝一杯；你若不请我，我便一个人去喝。 书生：小兄弟，看你年纪轻轻，不至于这么势利吧？ 飞云： Output is: 我是个读书人,不势利,我只想和你喝酒。 书生:小兄弟,你是不是有什么心事? Input is: 张无忌拿出屠龙宝刀，手起刀落，周芷若掉了一颗门牙，身旁的赵敏喜极而泣， Output is: 张无忌道:“你这是做甚么?”赵敏道:“我要你做我的丈夫。”张无忌道:“我不配。”赵敏道:“你不配,我也不配 Input is: 四川的省会是? Output is: 成都,简称蓉,别称“锦城”,是四川省省会,也是中国西部地区重要的中心城市,中国西部地区重要的经济中心、科技中心、金融中心和交通枢纽,中国西部地区重要的 ``` k=2 ```txt Input is: 书生：羌笛何须怨杨柳，春风不度玉门关。 飞云：（这诗怎么这么耳熟？且过去跟他聊聊如何。） 书生：小兄弟，要不要一起喝一杯？ 飞云：你请我呀？你若是请我，我便和你喝一杯；你若不请我，我便一个人去喝。 书生：小兄弟，看你年纪轻轻，不至于这么势利吧？ 飞云： Output is: 你这是在质疑本公子的人格?你若是不请我喝酒我就不去喝。你若不请我喝,那我就一个人走路。你要是不请我喝,那你便 ``` k = 3 ```txt Input is: 中国和美国和日本和法国和加拿大和澳大利亚的首都分别是哪里？ Output is: 分别在哪个省的哪个市 中国的北京,美国纽约,加拿大多伦多,日本的大阪,法国的里昂,澳大利亚的墨尔本,新西兰的基督城,澳大利亚首都堪 ``` # Finetune 目前只提供了不改变模型模型结构和数据格式的 finetune，也就是继续预训练。 ##### 1、准备训练数据 参考[数据](#数据)部分 ##### 2、模型切割 上面下载的模型是单机推理模型，所以在进行 finetune 的时候需要先对模型进行切割，切割成模型并行的模型。 参数： `model-parallel-size`：原始模型的分片个数，这里是 1 `--num-mp-model`： 切分后的模型个数 `--mp-model-save`：切分后，模型的保存路径 ```bash python tools/split_full_model_into_mp_model.py \ --model-parallel-size 1 \ --num-mp-model 2 \ --num-layers 31 \ --hidden-size 2560 \ --load /**ful model path**/ \ --mp-model-save /**mp model save path**/ \ --num-attention-heads 32 \ --max-position-embeddings 1024 \ --tokenizer-type GPT2BPETokenizer \ --fp16 \ --batch-size 1 \ --seq-length 1024 \ --model-type Pangu \ --vocab-file megatron/tokenizer/bpe_4w_pcl/vocab \ --finetune ``` ##### 3、训练 运行脚本: ```examples/finetune_pangu_distributed.sh``` ##### 4、模型合并 finetune 完后的模型是分片的，如果要进行单卡推理，则先需要对模型进行合并。 合并脚本： `--mp-model-parallel-size`：模型分片数 `--load`：模型保存目录 ```bash python tool/merge_mp_partitions.py \ --model-parallel-size 1 \ --mp-model-parallel-size 2 \ --num-layers 31 \ --hidden-size 2560 \ --load /full model ckpt dir/ \ --num-attention-heads 32 \ --max-position-embeddings 1024 \ --tokenizer-type GPT2BPETokenizer \ --fp16 \ --batch-size 1 \ --seq-length 1024 \ --model-type Pangu \ --vocab-file megatron/tokenizer/bpe_4w_pcl/vocab \ --reset-attention-mask \ --finetune \ ``` # 训练 参考脚本 ```bash examples/pretrain_pangu_distributed_2.6B.sh ``` # 数据 ##### 生成训练数据 参考脚本：`/tools/preprocess_data_pangu.py` 在 train_dataset 目录下存放多个 `xxx.txt` 文件，如果训练数据较多，最好每个 `txt` 文件大小统一，且分开多个 `txt` 存放，大小可以 10M 一个文件。如果有繁体文字，需要转成简体，可以使用`zhconv`。 每个 `txt` 文本格式为（需要空行分割不同样本）： ```txt sample 1 *** *** *** sample 2 *** *** *** sample 2 *** *** *** ``` ```bash python /tools/preprocess_data_pangu.py \ --input /train_dataset/*.txt \ --output-prefix /megatron/dataset/ \ --vocab-file /megatron/tokenizer/bpe_4w_pcl/vocab \ --dataset-impl mmap \ --append-eod ``` 将会生成/path/to/dataset/xxx.idx 和 /path/to/dataset/xxx.bin 文件。 Finetune 和预训练时需要填写参数：`--data-path=/path/to/dataset/xxx` pcl_pangu/pcl_pangu/model/panguAlpha_pytorch/README_mgt.md000066400000000000000000000750061443434453700240770ustar00rootroot00000000000000[Megatron](https://arxiv.org/pdf/1909.08053.pdf) is a large, powerful transformer developed by the Applied Deep Learning Research team at NVIDIA. This repository is for ongoing research on training large transformer language models at scale. We developed efficient, model-parallel, and multinode training of [GPT-2](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) and [BERT](https://arxiv.org/pdf/1810.04805.pdf) using mixed precision. Using our GPT-2 model we achieve a perplexity of 10.8 on the WikiText-103 dataset (improving SOTA from 15.8) and an accuracy of 66.5% on the LAMBADA datasets. For BERT training, we swapped the position of the layer normalization and the residual connection in the model architecture (similar to GPT-2 architucture), which allowed the models to continue to improve as they were scaled up. Our BERT models with 3.9 billion parameters reaches a loss of 1.16, SQuAD 2.0 F1-score of 91.7, and RACE accuracy of 90.9%. Our codebase is capable of efficiently training very large (several billion parameter) language models with both model and data parallelism. To demonstrate how the code scales with multiple GPUs we consider the following GPT-2 model sizes. All models use a vocabulary size of 51,200 and a sequence length of 1024. ![Cases](images/cases.png) The table below details the weak scaling from 1 to 8 GPUs of our model parallelism code in both a DGX-2 and a DGX-A100. Notice that we double the batch size on the DGX-A100 but the iteration time decreases compared to the DGX-2 resulting in a **2.1x** speedup for the end-to-end application. ![Model Parallel Scaling](images/scaling-mp.png) The following table details how Megatron scales using data parallelism in conjuction with model parallelism in a cluster of DGX-A100s. All of these cases use 128-way data parallelism and the scaling numbers are relative to a single A100 (Case 1B with a 1076ms iteration time). ![Data Parallel Scaling](images/scaling-dp.png)     # Contents - [Setup](#setup) - [Downloading Checkpoints](#downloading-checkpoints) - [Usage](#usage) - [Training](#training) - [Data Preprocessing](#data-preprocessing) - [BERT Pretraining](#bert-pretraining) - [GPT-2 Pretraining](#gpt-2-pretraining) - [Distributed BERT or GPT-2 Pretraining](#distributed-bert-or-gpt-2-pretraining) - [REALM Pipeline](#realm) - [Evaluation and Tasks](#evaluation-and-tasks) - [GPT-2 Text Generation](#gpt-2-text-generation) - [GPT-2 Evaluation](#gpt-2-evaluation) - [WikiText Perplexity Evaluation](#wikitext-perplexity-evaluation) - [LAMBADA Cloze Accuracy](#lambada-cloze-accuracy) - [BERT Task Evaluation](#bert-task-evaluation) - [RACE Evaluation](#race-evaluation) - [MNLI Evaluation](#mnli-evaluation) - [Datasets](#datasets) - [Collecting Wikipedia Training Data](#collecting-wikipedia-training-data) - [Collecting GPT-2 Webtext Data](#collecting-gpt-2-webtext-data) # Setup We officially support only python 3.6, pytorch 1.5, cuda 10, and nccl 2.6 versions and above. To use this repo please install the latest supported versions of PyTorch with GPU support and NVIDIA [APEX](https://github.com/NVIDIA/apex#quick-start). We strongly recommend using one of [NGC's recent PyTorch containers](https://ngc.nvidia.com/catalog/containers/nvidia:pytorch) (the latest compatible version at time of publication can be pulled with `docker pull nvcr.io/nvidia/pytorch:20.03-py3`). Data preprocessing requires [NLTK](https://www.nltk.org/install.html), though this is not required for training, evaluation or downstream tasks. To use megatron you can either clone the repo or install it via pip (make sure python3-dev is installed):